Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. I am an independent journalist with no financial interest, long or short, in any company mentioned here, including Walmart, Aaptiv, Aetna, Amazon (including PillPack and One Medical), Amwell, Andreessen Horowitz, Anthropic, Babylon Health, Berry Street, BetterHelp, Blueberry Pediatrics, Cabana, CheckUps, ChenMed, Cigna, Clover Health, Curai Health, Cost Plus Drug Company, CVS Health, Doximity, Eli Lilly, Elevance Health (Anthem), Epic Systems, General Medicine, GoodRx, Health at Scale, Hella+Health, Humana, Included Health (Doctor On Demand), JPMorgan Chase (Morgan Health), MeMD, Mercy, Molina Healthcare, Oak Street Health, Ochsner Health, QuadMed, Rock Health, Solace Health, Solv, Teladoc Health, Twin Health, UnitedHealth Group, VillageMD, Wheel and Zocdoc. Facts come from public records, company statements and press coverage, hyperlinked at the point of reference. Accounts attributed to people familiar with the company come from sources who spoke on condition of anonymity, and were verified against public records where possible. This article is my opinion, based on the sources cited, and is not investment, legal or medical advice.

While the world was mesmerized by Anthropic’s IPO filing on Monday (apparently everyone who has ever used the Claude chatbot is now an expert on S-1 forms 😉), we all missed the big news of Walmart’s 9th attempt at healthcare. The same day, Walmart expanded Better Care Services, its online healthcare hub, into a full “healthcare store” powered by General Medicine, a startup run by PillPack co-founder TJ Parker. Shoppers can compare insured and cash prices on more than 900 products and services, from telehealth and labs to select in-person visits, drawn from General Medicine’s catalog of 2,900+. It comes 2.5 years after Walmart closed all 51 Walmart Health centers and its virtual care business.

Walmart has decided that being the doctor doesn’t pay. But owning the aisle where you shop for one might.

But first, I’m excited to be attending HLTH USA 2026, the marquee healthcare innovation event, November 15-18 in Las Vegas! Join me: https://app.ingo.me/q/56jqv

🎙️ So… Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

OK, back to Walmart… Below, I go back through all 8 failures over the past 20 years, drawing on my 2024 inside scoop on the death of Walmart Health. And I name the names of the people who contributed to the failure of Walmart Health, along with what they promised in public while it was falling apart.

I don’t name names to be vicious, and I don’t enjoy making enemies. I name names because we, as an industry, need to learn from our mistakes, especially the big-name ones. And almost no one is paying attention to figuring out what exactly happened.

A note on how this works. Most of my investigations start with a tip from someone like you. I verify it with other witnesses, then add my own “mosaic theory” research on top. Everything stays confidential, always. I have been doing this for 3 years. What follows is a standing public service announcement that I run in every investigation. It is not directed at any company discussed in this piece, and nothing in this article alleges that Walmart or any of its employees or representatives has broken the law or engaged in unethical conduct.

If you see something unethical, or even just weird, anywhere in healthtech, do not stay silent. Tell a newspaper, tell me (DMs, Signal, WhatsApp, whatever you prefer), or, if you believe a law has been broken, tell a law enforcement agency (tips.fbi.gov).

Here is what’s inside:

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Disclosure: I am an independent journalist. I have no financial position in OpenEvidence, Doximity, Teladoc, Waystar, Clarivate, Alphabet (Google), RELX (Elsevier), Wolters Kluwer, Wiley, Epic, Abridge, Hippocratic AI, Anthropic, OpenAI, Baseten, Nvidia, Databricks, Perplexity AI, Hugging Face, SpaceX, Oura, Peloton, Hinge Health, Omada Health, Hims & Hers, Tempus AI, Veeva Systems, IDEXX Laboratories, Verily, Freenome, Commure, Atomic AI, Quadrivia AI, Heidi Health, K Health, UnitedHealth Group, Humana, Johnson & Johnson, Pfizer, Merck, Biogen, Incyte, Goldman Sachs, S&P Global, Softbank, FactSet, Evercore, Altaris, News Corp, The New York Times Company or any other company mentioned in this article or its charts. I have reached out to OpenEvidence and Daniel Nadler for comment multiple times and have received no response. The invitation to an interview on AI Health Uncut and on Digital Health Inside Out stands. Accounts attributed to people familiar with the company come from sources who spoke on condition of anonymity, and were verified against public records where possible. This article is my opinion, based on the sources cited, and is not investment, legal or medical advice.

In Part 1, I looked at the economics of healthtech M&A, with OpenEvidence as the case study: a company that claims $300M in ARR (annual recurring revenue), raised at a $15B valuation in September, and, per Business Insider, “could be open to an M&A deal” (M&A: a merger or acquisition). By any comparable I can find, the business is worth ~$500M or less.

This Part 2 is about the technology. If a buyer pays $15B, what exactly are they buying? A proprietary AI? Proprietary content? Proven accuracy? I looked for all three.

Part 2 is also about the people: how OpenEvidence and its CEO treat critics, and what a buyer would really get in the team.

To everyone who helped with this piece: thank you. It’s because of people like you that I continue doing this work.

Four things happened since Part 1:

• The Doximity lawsuit is over. On October 16, the day after Part 1 ran, a federal judge in Massachusetts dismissed OpenEvidence’s case against Doximity with prejudice. Each side pays its own legal fees, and the terms were not disclosed. Per Healthcare Dive, OpenEvidence was approaching a deadline to produce internal Slack messages that were allegedly deleted. Probably a coincidence. 😉 Either way, a pending federal lawsuit is exactly the kind of thing that spooks a buyer’s diligence team, and now there isn’t one.

• Another big-name announcement. On October 13, OpenEvidence and Johns Hopkins Medicine announced a “landmark collaboration” on medical education and “human-first” AI for patient care. No financial terms, and no research or validation commitments. As I showed in Part 1, OpenEvidence’s announcements have a way of landing right before the money does.

• OpenEvidence got its first JAMA paper. On October 15, the day Part 1 ran, JAMA published a study of how clinicians use OpenEvidence: 271M queries from 952,008 clinicians, including 556,200 physicians, between January 2024 and June 2026. Three authors are tied to OpenEvidence, including its chief medical officer, and the company supplied the data. It is a usage study. It counts questions, not correct answers. More in section 5.

• The FT profile. On October 20, the Financial Times ran a mostly glowing profile. Per a person close to the company, OpenEvidence booked $100M in “ad buys” for 2025, a metric that includes future advertising commitments, and is on track for $400M to $500M from ads by the end of 2026. Neither number is audited. Even on that projection, $15B is 30x to 37.5x a single year of ad sales. Nadler called OpenEvidence “the first viral consumer internet app in healthcare” and said “the end game is literature-based drug discovery,” worth “orders of magnitude” more than “whatever we could have charged hospitals or advertisers.” So the ad business that supposedly justifies $15B is now the small part of the story.

A note on how this works. Most of my investigations start with a tip from someone like you. I verify it with other witnesses, then add my own “mosaic theory” research on top. Everything stays confidential, always. I have been doing this for 3 years. What follows is a standing public service announcement that I run in every investigation. It is not directed at any company discussed in this piece, and nothing in this article alleges that OpenEvidence or any of its employees or representatives has broken the law or engaged in unethical conduct.

If you see something unethical, or even just weird, anywhere in healthtech, do not stay silent. Tell a newspaper, tell me (DMs, Signal, WhatsApp, whatever you prefer), or, if you believe a law has been broken, tell a law enforcement agency (tips.fbi.gov).

Here is what’s inside:

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Disclosure: I am an independent journalist. I have no financial position in OpenEvidence, Doximity, Teladoc, Waystar, Clarivate, Alphabet (Google), RELX (Elsevier), Wolters Kluwer, Wiley, Epic, Abridge, Hippocratic AI, Anthropic, OpenAI, Baseten, Nvidia, Databricks, Perplexity AI, Hugging Face, SpaceX, Oura, Peloton, Hinge Health, Omada Health, Hims & Hers, Tempus AI, Veeva Systems, IDEXX Laboratories, Verily, Freenome, Commure, Atomic AI, Quadrivia AI, Heidi Health, K Health, UnitedHealth Group, Humana, Johnson & Johnson, Pfizer, Merck, Biogen, Incyte, Goldman Sachs, S&P Global, FactSet, Evercore, Altaris or any other company mentioned in this article or its charts. I have reached out to OpenEvidence and Daniel Nadler for comment multiple times and have received no response. The invitation to an interview on AI Health Uncut and on Digital Health Inside Out stands. Accounts attributed to people familiar with the company come from sources who spoke on condition of anonymity, and were verified against public records where possible. This article is my opinion, based on the sources cited, and is not investment, legal or medical advice.

On September 24, Business Insider reported that OpenEvidence raised another $250M, this time from Andreessen Horowitz and a group of hospital systems, at a $15B valuation. Three paragraphs in, the real news: OpenEvidence “could be open to an M&A deal that includes access to compute” (M&A: a merger or acquisition).

Translation: the “ChatGPT for doctors” is for sale.

Now, back to OpenEvidence. The company claims $300M in ARR (annual recurring revenue), a self-reported number I will explain below. By any comparable I can find, the business is worth ~$500M or less. Nadler wants 30 times that. And given his Goldman Sachs pedigree and his investor list, he may just pull it off.

I have written about OpenEvidence many times, and I still cannot tell you with certainty what the company is. Neither can anyone outside it. I reached out to OpenEvidence’s media department and to Daniel Nadler directly, more than once. No response. So I did what I always do: I talked to people closely familiar with the company, and I did a whole lot of my own research. What follows is based on confidential sources, court filings, the company’s own statements, and more than a year of my prior reporting.

I name names. I hope you are sitting down.

A note on how this works. Most of my investigations start with a tip from someone like you. I verify it with other witnesses, then add my own “mosaic theory” research on top. Everything stays confidential, always. I have been doing this for 3 years. What follows is a standing public service announcement that I run in every investigation. It is not directed at any company discussed in this piece, and nothing in this article alleges that OpenEvidence or any of its employees or representatives has broken the law or engaged in unethical conduct.

If you see something unethical, or even just weird, anywhere in healthtech, do not stay silent. Tell a newspaper, tell me (DMs, Signal, WhatsApp, whatever you prefer), or, if you believe a law has been broken, tell a law enforcement agency (tips.fbi.gov).

In this Part 1 of my investigation, I examine the economics of healthtech M&A, using OpenEvidence as a case study.

In Part 2, coming next week, I’ll take a closer look at OpenEvidence’s technology and raise some serious questions about whether the company has a defensible competitive moat. Assuming, of course, I won’t be swimming with the fishes by then. 😉

Here is what’s inside:

Part 1 (today):

Part 2 (coming next week):

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I have no financial interest, long or short, in K Health, Hartford HealthCare, Cedars-Sinai, Atlantic Health, UVA Health, Mayo Clinic, Epic, Heidi, Abridge, OpenEvidence, Hippocratic AI, Commure, Ada Health, Infermedica, Doctorlink, OpenAI, or any other company mentioned here. Allon Bloch appeared on Digital Health Inside Out as an unpaid guest. K Health did not pay for, review, or approve this article. Quotes come from the recording of the October 1, 2026 interview, lightly cleaned up for readability. Facts come from the sources hyperlinked at the point of reference. This article is for informational and opinion purposes only and should not be construed as financial or investment advice.

On October 1, 2026, Alex Koshykov and I interviewed Allon Bloch, CEO and co-founder of K Health, on Digital Health Inside Out. Allon knew the questions would be hard. He came anyway and was a good sport for all 70 minutes. He never answered my 2 biggest questions. He did answer plenty of others, and some of those answers were better than I expected. Watch this rather spicy episode here.

Here is what’s inside:

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. I am an independent journalist with no financial interest, long or short, in any company mentioned here, including Nolla Health, Doctronic, Legion Health, General Catalyst, HATCo, Summa Health, Commure, Hippocratic AI, Heidi Health, Livongo, Teladoc, Mindstrong, SonderMind, Olive AI, Hims & Hers, OpenEvidence, SteadyMD, MDCalc, Mindgard, Aidoc, Clarium, Fabric, Judi Health, Transcarent and Verse Medical. Facts come from public records, company statements and press coverage, hyperlinked at the point of reference. Opinions are my own. Not investment advice.

Yesterday, Bloomberg reported that Nolla Health, a New York startup born in Norway, became the first company allowed to let software write new prescriptions in the United States. Topical acne drugs only, Utah only, for 1 year with an option for 2 more. CEO Luis Wenus called it the “first ever actual end-to-end AI doctor” in a post that drew almost 2M views. The approval came from the same Department of Commerce office that waved through Doctronic’s AI refills in January and Legion Health’s AI psychiatric refills in May. Nolla’s lead investor is General Catalyst, the firm that bought a hospital 12 months ago. God help us all. 🙏

But first, I’m excited to be attending HLTH USA 2026, the marquee healthcare innovation event, November 15-18 in Las Vegas! Join me: https://app.ingo.me/q/56jqv

🎙️ Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

Also, I encourage my readers to become Founding Members of my Substack. I need your support.

By joining, you’re not only supporting my continued investigations and research into healthcare AI. You’re also helping me make this work accessible to people who may not be able to afford a subscription, whether they’re students, between jobs, or simply going through a difficult financial period. (If that’s you, please reach out.)

That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to the generosity of these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Here is what’s inside:

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Once again, it’s time for some seriously spicy healthtech news from September on the Digital Health Inside Out podcast, the Digital Health Vitals edition. 🌶️🔥

But first, I’m excited to be attending HLTH USA 2026, the marquee healthcare innovation event, November 15-18 in Las Vegas! Join me: https://app.ingo.me/q/56jqv

🎙️ Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

On Monday, Sep 28, Alex Koshykov and I recorded the September Digital Health Vitals with special guest Patrick E. Donohue, founder of Hill Capital Corporation, a former healthcare analyst and banker whose next fund is all health tech. Stephanie Davis had to skip this one.

Watch the episode here.

And here is August’s recap.

TL;DR

• Oura postponed its $2.1B IPO on Tuesday, a day after we recorded. I called the 4x book weak on air.

• ChatGPT inside Epic is a distribution event, not a technology event.

• CommonSpirit paid $1.9B to leave Conifer, an RCM company. Patrick and I expect more insourcing.

• Who owns the clinician AI layer in 5 years? I said Epic. Patrick said foundation models.

• Thumbs: Heidi 3 up (mine with an asterisk), Tandem 2 up, Archy 3 up, Rightway split 3 ways.

Here is the episode outline:

1. Oura: Recorded Monday, Postponed Tuesday

• Alex’s setup: $2.1B, a book reportedly only 4x covered. The next morning Oura postponed, and I published Oura Is Peloton 2.0, and Not in a Good Way: 3 VCs selling 73% of the shares, $6.2M left for Oura after RSU taxes, and a 4x book against a 20x median. So much for lock-ups making the VC pump and dump impossible.

• Patrick: “It is eye-opening to see the majority of the proceeds going to selling shareholders.” Verdict: great private company, hard public company. “If only Oura can tell me I’m about to have an asthma attack, that’s worth a lot of money.” That product is in CEO Tom Hale’s keynote, not the S-1.

• My Peloton analogy: same 19.8% subscription share, same platform pitch, growth that stalls once enthusiasts have bought, as with Teladoc, Amwell and Cue Health.

• Alex’s analogy was Fitbit 2015, and he’s right: Fitbit popped 48% on day one and sold to Google at 63% below its IPO price.

• Wellness without the ring? Patrick: “A great company and a good investment can be two very different things.” His picks: National Beverage, Planet Fitness, Life Time. Mine: whoever the payer market pays, since there is no money in prevention. Alex: “Let’s all invest in our own health.”

  ---

2. ChatGPT + Epic: Distribution, Not Technology

• I got plenty of grief for calling it the end of the healthtech AI industry. Alex: “You use your stories as clickbait.” 😉

• The largest consumer AI product on the largest EHR, read-only because liability still belongs to the doctor. If “we integrate with Epic” is your moat, OpenAI’s relationship with Epic beats yours.

• Patrick: a wrapper is valid only with a deep vertical edge. In EHR, “I would be very concerned.” His analogy: Target built its e-commerce on Amazon, then had to leave. Amazon won. Target survived.

• Patrick’s filter: founders who design the human in the loop on purpose. My pushback: Hippocratic AI still has nurses babysitting AI conversations. Patrick: which 1%, 5% or 10% of the process needs a human?

• OpenEvidence’s founders are reportedly fielding acquisition interest. My sources say Daniel Nadler has wanted to sell almost from day one, and the Anthropic announcement is packaging for a buyer. The moat was never the technology, the Llama derivative or the 100% accuracy claims. The moat is the 40% of US physicians using it.

  ---

3. The 5-Year Bet: Epic or the Foundation Models

• Me: Epic, the easy bet. At UGM they covered everything, down to 10-clinician practices. Only antitrust would change that, and I actually hope it does.

• Patrick: foundation models. “This is Netflix coming in during the era of Blockbuster.”

• Alex: “It’s healthcare. It might be 20 years.” Not wrong. 😉

• One agreement: frontier models beat fine-tuned wrappers, as I wrote in August 2024 and when fine-tuning died. My bet: they partner with Epic rather than replace it.

  ---

4. CommonSpirit Paid $1.9B to Leave Conifer

• CommonSpirit pays ~$1.9B over 3 years to exit Conifer 6 years early. Conifer lays off 1,037 in November.

• After 14 years with a specialist, CommonSpirit’s cost to collect was double the industry average. Paying $1.9B was cheaper than staying. 🤯

• Everyone else is buying RCM: R1 at $8.9B, Ensemble at $12B, Commure at $7B, and Waystar exploring a sale after buying Olive AI’s remains. Somebody’s math is wrong.

• Patrick expects more: “The ROI calc has literally changed dramatically in the last 6 to 12 months. Now Claude and GPT are just doing the models outright.” My caveat: real AI adoption in healthcare is 10%, and “autonomous” coding often means Bangalore.

  ---

5. Thumbs 👍🏼, Thumbs 👎🏼: Heidi, Tandem, Archy, Rightway

• Heidi, $340M at $900M. My asterisk: still no peer-reviewed study, and $240M of it is General Catalyst lending against revenue. Patrick pushed back: founders protecting equity, and 70% non-dilutive means the lender trusts the revenue. “Stuff I see all the time that never hits the headlines.” Heidi stays a strong maybe.

• Tandem Health, $100M Series B. Me: up. 3 EU MDR Class IIa devices, no Epic to depend on. Abridge should be frightened: it is not FDA cleared. Alex’s counter, via Nabla’s Alexandre LeBrun: once cleared, every release needs FDA sign-off, and scribes ship every 3 days. Patrick: neutral, a U.S.-only investor.

• Archy, $50M Series C. All up. Dental is fragmented, and Archy takes a cut of $300M in payments. Me: It’s Stripe for dentists.

• Rightway, $155M at $1.75B. Patrick: up, PBMs are “ripe for disruption.” Alex: down. Me: neutral, though the PBM revolt is overdue. Its moat is transparency, and the Consolidated Appropriations Act makes 100% rebate pass-through law for employer plans by 2029. When transparency is the legal minimum, what’s left of that moat? 🤷🏼‍♂️

Catch the full episode here:

👉🏼 And don’t forget to subscribe. 😉

Upcoming events: Patrick’s Hill Capital Summit is Nov 12 in St. Paul. Alex and I are recording at HLTH, Nov 15 to 18 in Las Vegas. See you there. Daniel Nadler, the invitation stands. 😉

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then, go tell your friends!

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👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. I am an independent journalist with no financial interest, long or short, in any company mentioned here. I did not contact Oura for comment. Facts come from SEC filings and press coverage, hyperlinked at the point of reference. Opinions are my own. Not investment advice.

It’s almost as if Tom Hale, Oura’s CEO, read my September 5 piece, “Oura IPO: Slick Gadget, Shaky Business,” and decided not to test my thesis in public markets just yet. 😉

🚨 But first, an announcement.

🎙️ Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

By the way, if you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Oura postponed its IPO this morning. It was set to price tonight and begin trading on Nasdaq as OURA tomorrow. In its announcement, Oura blamed “uncertainty in the IPO market,” despite “strong demand.” It says the business “has further strengthened since beginning the IPO process” and expects 5.7M paid members by the end of its fiscal year tomorrow. CEO Tom Hale said Oura has “the luxury of choosing our moment.” No new date was given.

Uh, OK… But we were not born yesterday.

Let’s get real. The demand was super weak. Bloomberg reported the IPO shares book was only ~4x oversubscribed (boooo 👎). Eli Lilly and Dragoneer had indicated interest in up to $400M of stock between them. As I show in Section 10, 4x is very weak for a deal this size.

On September 5, I read Oura’s S-1 and called it Slick Gadget, Shaky Business. I also put a three-part prediction on record. Tonight was supposed to test the first part. The amended filing from September 21 answered the question the first one left open: who receives the IPO money. The postponement raises a new question:

The filing also reminded me of another company. In September 2019, a connected-hardware brand with triple-digit growth, a subscription story, and a devoted early customer base went public at $8.1B. It was Peloton.

🚨 TL;DR:

• Oura postponed its IPO on the day it was set to price, blaming “uncertainty in the IPO market.” Its subscription book was only ~4x covered.

• Subscriptions were 19.8% of Peloton’s revenue in its IPO year. Subscriptions are 19.8% of Oura’s today, and that share has little room to grow.

• Peloton grew 110% the year it listed. Revenue has fallen every year since its fiscal 2021 peak, and its market value is down 96%.

• Both learned that hardware breaks. Peloton had recalls. Oura booked $84.4M of Ring 4 warranty expense in a year it earned $12,000.

• Oura is profitable. Peloton was not. But Oura’s 85% retention trails Peloton’s 95%, and I doubt even 85% will hold.

• Three VCs were set to sell 73% of the IPO shares, Forerunner its entire stake. After employee RSU taxes, Oura would have kept just $6.2M.

• Only 3 of 123 US IPOs since 2019 spent nearly all their proceeds on RSU taxes. None paired it with as much insider selling as Oura planned.

• At the IPO midpoint, Oura would have been worth ~2443x its 2015 seed valuation and ~20.5x Forerunner’s 2020 round. Three early VCs were heading for the exit. In my view, that was a horrible signal. Private rounds are easy to inflate. Millions of public shareholders are harder to fool.

• A 4x book is weak. The typical large US IPO since 2023 drew 20x. Only Reddit and SpaceX, which raised $86B, came close to Oura’s 4x. Waiting costs Oura little. Its employees pay the price, with RSUs stuck on ice. So do the selling VCs, with ~$1.53B of sales on hold.

• At ~10x to 11x revenue, Oura’s IPO range was stretched, but not crazy. Peloton listed at ~9x.

• On the IPO plan, Oura looks more like Fitbit, which popped 48% on day one and sold to Google 4 years later, 63% below its IPO price.

• Oura’s “predictive health” looks like an unrealized vision from good people, not deception. But the IPO was priced on the vision.

• Peloton had Covid. Oura has AI. My September 5 prediction is on hold with the IPO, but I’m standing by it, Finnish jewelry and all. 😉

Here is what’s inside:

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I have no financial interest, long or short, in Nvidia, OpenAI, Anthropic, Alphabet, xAI, Meta, Insilico Medicine, Recursion, Isomorphic Labs, Novartis, GRAIL, or any other company mentioned here. This article is for informational and opinion purposes only and should not be construed as financial or investment advice.

🚨 But first, a quick personal note…

Last week I spoke at the inaugural Origin Point conference in La Jolla, and I’ll be as candid about it as I am about everything else: it was superb. About 85 people, lots of investors, lots of founders, and small enough that you could have real conversations instead of collecting badges. We covered everything from longevity, GLP-1s, peptides and biomarkers to consumer healthcare AI, wearables, AI agents, women’s health, M&A, and, fittingly for this issue, AI’s potential role in curing cancer. Thank you to the organizers, , Callie Baker and , and to our panel moderator Julia Croxen and my fellow panelists and Anson Whitmer for a lively discussion on digital health, consumer health and AI products. Also, black caviar for breakfast. 😉 I posted more on LinkedIn. If you get the chance to attend next year, definitely do it.

By the way, if you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

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On September 6, Jensen Huang posted that OpenAI’s new GPT-6 Astra means “AGI has arrived.” Three days later, launched CancerBench, a leaderboard with one metric: how many types of cancer has your frontier model cured? GPT-6 Astra scored 0. So did Claude Fable 5.1, Gemini 3.8 Flash, Grok 4.6, and Muse Spark 1.3. “All tied for first. And last.”

Is it AGI if it scored exactly zero on CancerBench? 🤔

On September 7 I published There Is No Money in AI Drug Discovery. The most common follow-up question was about cancer. This is the answer.

TL;DR

• CancerBench: 5 frontier models, 0 cancers cured, days after Nvidia’s CEO declared AGI. The promises behind the joke were serious: Amodei’s “~5-10 years,” Hassabis’s “next decade,” Altman’s “[choice] to cure cancer.”

• AI-discovered molecules clear Phase 1 at 87.5% (21 of 24), against 52.0% historically. In Phase 2 they clear at 40% (4 of 10), no better than anyone else. None has finished a Phase 3. For cancer, the AI-specific Phase 2 record is 0 trials.

• Oncology, the largest field in drug development, has among the worst odds: a 5.3% chance of approval from Phase 1, against 7.9% for all drugs, and a 24.6% Phase 2 pass rate, its worst phase.

• Mayo Clinic’s AI-designed pancreatic cancer drug, GIPCi, is real, preclinical, and not yet in Phase 1. From there, the average oncology clock to approval is 10.3 years.

• Human trials cannot be simulated, and the FDA has cleared exactly 0 generative AI products and has no pathway for one. My estimate: 10 to 20 years, not 5, and probably not with transformer AI.

• Yesterday, on September 23, an FDA panel voted 6 to 4 that GRAIL’s machine-learning blood test Galleri is effective. It finds 13.6% of stage I cancers and 60% of stage IV. That is the AI-and-cancer frontier right now: a detection test, on a split vote.

• Why the molecule keeps winning and the patient keeps losing. A 50-year cardiologist’s essay in The Pharos, and the FDA’s own record on cancer drugs approved on proxies: of 36 approved 2008 to 2012, 5 later proved to extend life. AI optimizes the part. The trial judges the whole.

Here is what’s inside:

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Apologies to my regular readers, but I ought to respond to a lot of nonsense circulating on the Internet right now about the Navier-Stokes Millennium Prize Problem. Unfortunately, a good deal of that nonsense is coming from OpenAI itself. I want to set the record straight. Apologies in advance for getting technical at times, but there is really no other way to address an issue this important.

What do a NASA scientist, an oil engineer, and a meteorologist all have in common? None of them gives a sh*t about OpenAI’s Navier-Stokes announcement, even though all three run the Navier-Stokes equations in their models every single day.

TL;DR:

• On Sep 8, 2026, OpenAI claimed that roughly 10,000 AI agents proved the 3D Navier-Stokes equations can reach infinite velocity in finite time. OpenAI won’t claim the $1M prize, the Clay Institute still lists the problem as open, and 25 Fields medalists signed a declaration on the “severe misalignment” of AI in mathematics.

• Assume, for the moment, the proof actually holds. For NASA, aircraft makers, pipeline operators, and weather models, the near-term impact rounds to 0. Not 1 practitioner announced a model change. Fields Medal winner Dr. Terence Tao said as much five days before the announcement: a blow-up “would not radically transform” how we model weather or climate.

• Engineers never solved Navier-Stokes analytically. They approximate it numerically, validate against wind tunnels and flight data, and have handled its instabilities for decades. Those calculations already work.

• The practical problem was solved decades before the mathematical one.

Here is what’s inside:

1. Everyone Is a Navier-Stokes Expert This Week

It’s funny how everyone became an expert in Navier-Stokes overnight. I spent the past week on the sidelines, listening. There are two camps. One says AI is now so good that you hand it a problem open since Leray in 1934 and it spits out a proof. The other says slow down. Both camps are mostly people who have never solved these equations for a living.

I have. I’m a mathematician and researcher who used Navier-Stokes in my academic work, and I know a bit about the practical side. Back in the day, I worked on a couple of fluid dynamics projects with my father, Viktor Polevikov, who has spent his career studying fluid dynamics, magnetic fluids, ferrofluids, heat convection, heat and mass transfer, and gravity. He has published more than 200 peer-reviewed papers, with applications across a wide range of industries and in research relevant to organizations including NASA. That work came out of the Heat and Mass Transfer Institute in Minsk, a world-renowned research school that published two internationally recognized journals in thermo- and hydro-dynamics and, over the decades, employed thousands of experts from around the world, including researchers from the U.S., UK, Germany, France, and Japan.

In June I wrote that SpaceX’s success is bad news for healthtech because Wall Street now pays for real engineering moats. After a week of hot takes, I’d add a corollary:

Being good at code and riddles does not make you good at physics.

I call this “AI tourism”. Fluid dynamics just had its first tourist season. Programmers who are not physicists went looking for famous unsolved problems, found a 92-year-old one, let a model run on it for days, and built a story about cracking it. A private company’s valuation and its position in the model race both benefit from that story. The substance is thinner than the hype.

2. The Navier-Stokes Equation, in Plain English

Navier-Stokes is Newton’s F = ma written for a blob of fluid instead of a cannonball. The left side is the acceleration: how fast the blob’s velocity u changes over time, plus how the blob gets carried along and stretched by the flow around it. The right side is the forces: pressure p pushes the blob from high to low, viscosity ν smooths out differences the way honey resists a spoon 🍯, and f is any outside push, a pump, a fan, gravity. The shorter line on top says the fluid can’t be squeezed. That’s the whole thing. The Millennium question asks: start with a smooth flow, does it stay smooth forever, or can it spike to infinite speed at a point? OpenAI’s answer is that with the right smooth outside push, it spikes. Note the f at the end of the equation. The result needs forcing. Whether the equations blow up with no outside push at all, the version most people picture, is still open.

3. Briefly on the Soap Opera, Then Let’s Move On

The facts behind this jaw-dropping soap opera. OpenAI says its agents needed 88 hours to find the proof and 17 more to formalize it in Lean, an AI proof assistant. It then published the result on its own website, the way you or I publish a blog post. No journal, no editor, no referee. 🤦‍♂️ The Clay Institute’s rules require publication in a qualifying outlet, a 2-year waiting period after that, and general acceptance by the mathematics community. So the earliest anyone can call this “solved” is 2028. NYU’s Tristan Buckmaster says OpenAI fought dirty. Everyone built on the forcing method of Diego Córdoba and Luis Martínez-Zoroa, whom Princeton’s Charles Fefferman called “the heroes”. Buckmaster and Anthropic’s Levent Alpöge posted their own AI-assisted blow-up proofs for the Euler equations a day earlier. OpenAI even admits it “cannot rule out” that de-identified data from Buckmaster’s use of its products helped its models. (I.e., there is a real possibility that OpenAI used mathematical research shared in ChatGPT conversations without the researchers’ knowledge or consent.) Who typed the prompt first is irrelevant to me. And on Sep 11, 25 Fields medalists, from Terence Tao to Peter Scholze, declared that “solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight.”

Amazingly, Tao either knew something was up or somehow saw this coming. On Sep 3, five days before the OpenAI announcement, he described the exact scenario: an autonomous AI harness with enormous compute runs the whole search internally, the company keeps the process out of public view, and “technically, one of the most prominent open problems in mathematics would now be solved,” with “almost no value added to mathematics as a consequence.” Problems like Navier-Stokes regularity are posed, in his words, “not because we desperately want the solution to these problems in and of themselves,” but because the human effort to solve them builds the field. Solving one prematurely by purely AI-powered methods, without transparency into the process, “can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.” Two days later he added that there is “a substantial opportunity cost in converting a historically productive and motivating problem into a mere viral social media post advertising some benchmark progress.”

If that sounds familiar, it’s because I’ve been making the same argument about medical AI benchmarks for 2 years. A 100% score on a hard benchmark (or a riddle) is not understanding. It is most likely cheating.

OK. Now assume the OpenAI proof survives. Practically, what changes?

4. What NASA Actually Does With Navier-Stokes

Start with what didn’t happen. Nobody at NASA woke up on Sep 9, read the paper, and called an emergency meeting. Not one NASA scientist, oil engineer, or meteorologist announced that a model has to change. I’d bet most of them didn’t read past the headline.

NASA doesn’t solve Navier-Stokes analytically. Nobody does. As NASA Glenn puts it, the equations are “too difficult to solve analytically,” so engineers solve approximations on computers. That’s computational fluid dynamics (CFD).

The Smithsonian displays the engineer’s version of the equations a short walk from the hardware they flew: five conservation laws (mass, momentum in three directions, energy), with density and heat terms that the incompressible textbook form above leaves out. That is the version a rocket or a cruise missile gets designed with.

To model airflow around a rocket, a computer carves the surrounding space into millions or billions of cells:

rocket → grid → approximate Navier-Stokes in each cell → step forward in time → read off pressure, temperature, velocity, turbulence

NASA’s OVERFLOW and FUN3D codes do exactly this for aircraft, launch vehicles, and Artemis, on grids that run into the billions of cells. NASA’s own CFD Vision 2030 study calls CFD central to aerospace design for decades.

And here’s the important thing. Those calculations already work. Engineers check them against wind tunnels, flight data, and previous designs. Thousands of NASA scientists solve this equation under an enormous range of conditions, singularities included.

So the OpenAI result doesn’t mean “holy sh*t, all our CFD was wrong.” It means something far subtler.

5. Imagine NASA Simulating a Capsule at Mach 20

A capsule enters an atmosphere at Mach 20:

air → compression → shock wave → enormous heating → flow around the vehicle

For the Mars 2020 entry capsule, NASA ran two independent Navier-Stokes solvers, DPLR and LAURA, and used the resulting heat-transfer rates to validate the heat shield design. Perseverance landed. That’s the validation.

OpenAI’s result does not show those calculations are wrong. It shows that some mathematically permissible Navier-Stokes configurations can evolve into singularities: a vortex that, in OpenAI’s own words, “spirals inward and gets increasingly elongated, like spaghetti,” until velocity becomes unbounded. That is very different from showing that a CFD run of an Artemis rocket is secretly approaching a singularity.

6. We Knew About the Swirls All Along

OpenAI’s pictures of spiraling vortices are pretty. They are also not news. Mathematicians and engineers have known for decades that these equations have regions of singularity and instability, and there are decades, arguably centuries, of techniques for handling them numerically. Entire careers were built on it. The most serious theoretical treatment is still Olga Ladyzhenskaya’s book, written by a pure theorist who knew exactly what practitioners were doing with the equations.

Here is what instability looks like in practice, from heat convection, the field my father spent his life on. Solve a convection problem at a low Rayleigh number, Ra (the dimensionless number that measures how hard buoyancy is pushing), and you get one big circulation cell. Push the Rayleigh number up and secondary cells appear. Around Ra ≈ 10⁷, tertiary cells show up: small-scale structures, but still steady in time, still fully described by Navier-Stokes. The structure of the motion changes quantitatively and qualitatively as you approach the crisis of laminar convection. Past that point the flow turns turbulent, and turbulence is a stochastic process: i.e. solve the same problem today and tomorrow and you get two different solutions. Nobody has “solved” turbulence, and nobody may ever solve it in the traditional sense, because you cannot solve a probability distribution the way you solve an equation.

What engineers do instead is average. The small turbulent cells arise spontaneously in time and space, their velocities sit 2 to 3 orders of magnitude below the main flow, and they barely move the temperature field. So you solve for the mean flow numerically, treat the pulsations as small-scale noise, and validate against experiment. My father computed the integral heat flux (the Nusselt number) against Rayleigh number well past the laminar regime, published it in an international journal, and nobody questioned the results, because the curve continued smoothly out of the laminar range. That is the practical answer to “velocity goes to infinity.” At the point where the laminar model breaks, a different model takes over.

Physically, infinite velocity is nonsense, and every practitioner knows where to stop trusting the continuum equations.

7. A 6th Reason Your Simulation Blew Up

CFD engineers deal with runs that go bad all the time:

extreme turbulence → smaller structures → finer mesh → numerical instability → solver blows up

Historically, the checklist of explanations had five items:

1. mesh not fine enough,

2. turbulence model inadequate,

3. time step too large,

4. numerical method unstable,

5. physical assumptions wrong.

OpenAI adds a 6th: the equations themselves can produce a singularity.

That changes what we know the equations are capable of. It does not mean every CFD failure is a singularity. Far from it. As Quanta notes, real fluids are molecules, not a perfect continuum, and at small enough scales molecular physics, compressibility, chemistry, or plasma physics take over. Years from now, this could yield a sharper criterion for when to switch models. That would be valuable. OpenAI’s proof is nowhere near handing NASA that criterion.

8. Oil Pipelines and Weather

A pipeline engineer wants: pressure here → flow rate → friction → pressure downstream.

How oil behaves when it accelerates and decelerates inside a pipe is Navier-Stokes, simplified to one dimension and validated against decades of field data. Same for reservoirs, drilling mud, pumps, and compressors.

Weather models don’t even run the full thing: ECMWF’s operational forecast discretises a hydrostatic version of the Euler equations. Don’t take my word for it. Take Tao’s. In the same Sep 3 thread he wrote that the regularity problem “is not important for its direct physical application,” that computational fluid dynamics “is already a mature subject, deployed extensively in the atmospheric sciences,” and that a pathological instance of blow-up “would not radically transform the way we would, for instance, model weather prediction or climate change.”

An equation doesn’t need to be perfect under every conceivable condition to be extraordinarily useful within a physical regime. Newtonian mechanics is “wrong” at relativistic speeds. We still build bridges with it. The ideal gas law isn’t universal. Chemical engineers use it daily.

9. The Rockets Flew First. The Proof Came Later.

The practical problem was solved long before the mathematical one. NASA was flying vehicles designed with numerical Navier-Stokes while theoretical mathematicians couldn’t prove the equations stayed well behaved.

The Millennium problem never asked “can we use Navier-Stokes?” We knew we could. It asked whether we understand what these equations are mathematically capable of doing. If the proof holds, the answer is: not quite.

Which is why the loudest people last week were not the ones whose rockets depend on the answer. As I wrote when ChatGPT Health plugged into Epic, frontier labs will keep winning riddles. Domain experts still decide what the riddles are worth.

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then—go tell your friends!

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👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. I have no financial interest, long or short, in Tenet, CommonSpirit, or any other company mentioned here. This article is journalism and opinion, not investment advice.

🚨 First, an announcement.

🎙️ Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

🚨 TL;DR:

• On September 2, Tenet’s revenue cycle management (RCM) subsidiary Conifer Health Solutions filed a WARN notice for 1,037 permanent layoffs, effective November 2. The notice cites “restructuring and technology initiatives” and the end of services to CommonSpirit Health on October 30.

• CommonSpirit is paying Tenet about $1.9B over three years to break a contract that ran through 2032. Tenet booked $413M of it as revenue in Q2 and called the deal highly accretive. CommonSpirit booked a $2.2B charge and a $3.4B quarterly net loss. Conifer is cutting 1,037 people.

Everyone declared victory. Healthcare is very good at that. 😉

The part nobody says out loud: the entire pitch of outsourced revenue cycle is that a specialist at scale bills more cheaply than a hospital can. CommonSpirit just paid $1.9B to say “F*ck, no.” It did so in the same 12 months in which private equity and VC bought, merged, or funded nearly every RCM asset with a pulse.

And CommonSpirit is not alone. Adventist Health also effectively said “F U” to its expensive outsourced RCM operation.

Insourcing could be the next big trend in RCM, with health systems deciding they may not need expensive middlemen like Tenet, Oracle, and Commure after all.

🚨 Here is what’s inside:

If you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I have no financial interest, long or short, in BioXcel Therapeutics, Teva, Novartis, Ionis, Amgen, Eli Lilly, Novo Nordisk, Recursion, Relay Therapeutics, Absci, AbCellera, Schrödinger, Insilico Medicine, Isomorphic Labs, or any other company mentioned here. This article is for informational and opinion purposes only and should not be construed as financial or investment advice.

Happy Labor Day! 🇺🇸

I hope you’ve got some time this long weekend to relax, enjoy the last days of summer, and spend time with the people you care about.

And thank you, as always, for reading and supporting AI Health Uncut. ❤️

🚨 First, a quick personal announcement…

I’m honored to be speaking at the Origin Point healthcare technology conference in beautiful La Jolla, CA, on September 16–18, 2026. If you’re there, please come say hello!

On August 27, 2026, BioXcel Therapeutics filed for Chapter 11 in Delaware. The next day it announced that Teva had agreed to be the stalking-horse bidder for substantially all of its assets.

I have written a lot of these autopsies. Babylon. Olive AI. Cue Health. Carbon Health. I predicted the wave in January 2025.

This one is worse for the AI story, for one reason. BioXcel did the hard thing. It got a drug approved. IGALMI, a sublingual dexmedetomidine film for agitation in schizophrenia and bipolar disorder, has been FDA approved since 2022. Most of the AI drug discovery industry has never come close.

And BioXcel still went bankrupt…

🚨 TL;DR

• BioXcel Therapeutics got an AI-branded drug through the FDA and filed Chapter 11 on August 27. IGALMI booked $182,000 of net product revenue in the second quarter of 2026, against a $14.7M quarterly loss and a $747.5M(!) accumulated deficit. That’s quite a chunk of cash that went up in smoke.

• Teva’s widely repeated “up to $125M” is $57.5M of guaranteed upfront cash. Everything else is regulatory and sales milestones, and the real theoretical ceiling is $145M.

• BioXcel’s own 10-Q says the EvolverAI engine was created and owned by a related company run by the same CEO, that the public company’s option for further work expired unexercised, and that no development happened through it after 2024. The bankruptcy announcement still calls BioXcel a company “built on artificial intelligence.”

• I look at 5 years, 6 companies, $458.7M of revenue against $5.58B of losses. $12 burned for every dollar booked, and zero FDA approvals to show for it.

• The rest of the sector is no better. In the second quarter of 2026, Relay lost $239(!) for every $1 of revenue, Absci $104, BioXcel $81, Recursion $17. Schrödinger, which mostly sells software, is the only name that made money.

• Six years after the first “AI-designed” molecule entered a human, exactly one is in Phase 3, and it is in China. None has been approved by the FDA.

• I spent many years in quant investing, and every quant learns the same lesson. You cannot predict one trial or one stock, but you can know the shape of the distribution, and for “one drug wonders” it is fat-tailed, right-skewed, and the median draw loses. Biotech and pharma with revenue returned +10.4% a year from 1996 to 2023. Without revenue, negative 9.3%. The average is pulled way up by a handful of rare “one-hit wonders.” For the typical investor, the actual experience is losing money.

• On Friday, Novartis lost an 8,323-patient Phase 3, five weeks after Novo Nordisk lost one of its own. Both drugs moved their biomarker exactly as designed and neither reduced cardiovascular events. Neither is an AI drug, and that is kind of the point: the molecule was never the hard part.

• The 2024 Nobel was well deserved. It also got us excited about the seemingly endless possibilities of AI in drug discovery and DNA and genomics research. It also demolished the barrier to entry. 842 venture rounds have walked through that door since the start of 2024, and produced zero approved AI-discovered drugs.

• AI compresses the cheapest step in the chain. Phase 1 is about 5% of R&D cost. Phase 3 alone is about 52%.

• Why hasn’t AI cured cancer (yet)? An oncology drug entering Phase 1 has just a 5.3% chance of ultimately being approved, and oncology’s Phase 2 transition rate is the worst of any clinical stage. That matters because Phase 2 is where a drug has to show that it actually works in patients. AI may be helping us find candidates faster, but it has not yet meaningfully improved that efficacy bottleneck.

🚨 Here is what’s in the rest of this piece:

If you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I have no financial interest, long or short, in Oura, Whoop, Ultrahuman, Dexcom, Apple, Google, or any other company mentioned here. I did not contact Oura Inc., its subsidiaries, or any of their officers, directors, agents, or representatives for comment prior to publication. No such request was warranted. The factual assertions in this article are drawn from three categories of material. First, publicly available sources, including Oura’s Form S-1 registration statement filed with the U.S. Securities and Exchange Commission (SEC), Oura’s own published materials, and contemporaneous press coverage, each of which I have hyperlinked at the point of reference. Second, my own independent research and analysis. Third, first-hand accounts voluntarily provided to me by Oura members regarding their personal experience with the product. I present those member accounts as reported experiences, not as findings of fact, and they have not been independently verified by Oura or by any third party. Where I express opinions, interpretations, inferences, or conclusions, they should be understood as such. This article is for informational and opinion purposes only and should not be construed as financial or investment advice.

🍁 Happy Labor Day weekend! 🇺🇸☀️

I hope you’re getting at least a little time to unplug, spend time with family and friends, enjoy the last stretch of summer, and avoid thinking about healthcare AI, venture capital, IPOs, or whatever other madness usually fills our feeds.

I, apparently, failed at that last part. So here we are. 🙂

On Thursday, September 3, 2026, Oura filed its S-1 with the SEC, the detailed registration statement companies file before going public. Ticker “OURA” on Nasdaq. Goldman Sachs, Morgan Stanley, JPMorgan and 15 more banks on the cover. The reported target: a $16B+ valuation and up to $3B raised.

Nice timing, too. While the entire financial internet argues over whose IPO lands first, Anthropic’s or OpenAI’s (Polymarket says Anthropic, 96%), Oura is trying to quietly squeeze a $16B ring through the side door before anyone looks up from the betting slip. 😉

But first, a thank you message to my paid subscribers. I make a fair amount of my work available for free. But research-intensive investigations, reviews, and analyses take time, effort, and resources. So I’m enormously grateful to everyone who chooses to support this work with a paid subscription, and especially to my Founding Members.

Founding Members receive some additional benefits, of course. But more importantly, they are supporting the broader mission behind AI Health Uncut. Their support also helps make it possible for me to keep much of my work accessible to students, people between jobs, and others who may not be able to afford a subscription right now.

Become a Paid Subscriber

One thing Substack offers paid subscribers is the option, once they subscribe, to share a few thoughts about why they decided to support the publication. They can do that publicly or privately. I’ve received so many of these messages, and most of them have been incredibly kind. Here’s a recent one that particularly stood out to me, shared with the author’s permission, of course:

Thank you for such kind words, . 🙏

OK, back to Oura’s IPO ambitions…

A word on my credentials. I used to be a quant investor, not without some serious mistakes. These days, I’m an independent journalist, healthcare analyst, and healthcare AI startup founder. So I think I’m somewhat (maybe? 😉) qualified to read this S-1, and, as my prediction record shows, equally qualified to be wrong about it. 😉

I’ll say the nice thing first, because it’s true. I love the Oura ring. And Oura is the rare healthtech company that shows up to its IPO with $1.4B in trailing revenue and an actual net profit.

Then I read the risk factors. Page after page, the company that sells “predictive health” tells the SEC what it will not promise you. That’s pretty much the article in a nutshell. 😉

🚨 TL;DR:

If you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I have no financial interest, long or short, in OpenAI, Epic, OpenEvidence, Abridge, Doximity, UpToDate, Heidi, or any other company mentioned here. This article is for informational and opinion purposes only and should not be construed as financial or investment advice.

🚨 TL;DR:

• On September 1, 2026, OpenAI announced that ChatGPT for Healthcare now connects directly to Epic, the EHR oligopoly holding records for 325M+ patients. Clinicians can summarize charts, pull history, review labs, and prep for visits without leaving the patient record.

• OpenAI also shipped a Healthcare Public Data plugin wired into nine official sources, including ClinicalTrials.gov, CMS Coverage, RxNorm, DailyMed, and PubMed. Eligible U.S. ChatGPT for Clinicians users can install it on their own. Inside an enterprise workspace, an administrator has to switch it on.

• That is the entire pitch deck of literally a thousand healthtech startups. “AI that plugs into your EHR and saves clinicians time.” Absorbed in one press release.

• None of this is a technology breakthrough. It is a distribution event. And let’s be real. (We should probably say it again, because smart people have been saying it for years.) Distribution was the only real moat these companies ever had..

• The survivors will be the ones with deep workflow integration, highly specialized medical data, regulated write-back capabilities, and the willingness to own liability. Not the ones with a prompt and a RAG bolt-on. Duh…

🚨 Here is what’s in the rest of this piece:

If you cannot afford this article, perhaps you’re a student or currently between jobs, please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations from these wonderful individuals, I’m able to provide access to anyone who needs it.

Become a Founding Member of My Substack

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist and constitutes journalism, commentary, and opinion protected by the First Amendment to the U.S. Constitution and other applicable principles of freedom of speech and freedom of the press. Those protections do not eliminate the responsibility to report accurately, and I take that responsibility seriously. I endeavor to base factual statements on verifiable information, primary-source documentation, publicly available records, interviews, and other evidentiary materials. Where I express opinions, interpretations, inferences, or conclusions, they should be understood as such and are based on the information available to me at the time of publication.

I have no financial interest, whether long or short, direct or indirect, in any of the companies discussed in this article. I have not received compensation from any person or entity for publishing this article. Nothing in this publication constitutes financial, investment, legal, or other professional advice.

Can you feel it?

That crisp autumn air. The leaves beginning to turn. And wait… do you hear that? Somewhere in healthcare, another upcoding allegation is quietly poking its head above the soil.

Then another.

And another.

Ah, fall. 🍂 My favorite season. Pumpkin spice, shorter days, and fresh reminders of what can happen when AI, reimbursement incentives, and healthcare collide.

Come along with me as we wander through this charming little landscape of AI, upcoding, and alleged criminal conduct.

🚨 But first, a couple of quick personal announcements…

• I’m honored to be speaking at the Origin Point healthcare technology conference in beautiful La Jolla, CA, on September 16–18, 2026. If you’re there, please come say hello!

• 🎙️ Calling all medical professionals, healthtech folks, and fans of our podcast, Digital Health Inside Out. Alex Koshykov and I are looking for a few brave souls willing to join us for in-person conversations that may range from serious healthcare debates to completely inappropriate healthtech dad jokes. 😄 We’ll be recording right from the floor of HLTH in Las Vegas, November 15–18. 🎰🔥 If you’ll be there and think you’d be a good sport, drop a comment or send either of us a DM. One thing we can promise. It won’t be boring. And yes, things may get a little heated from time to time. 😉

• And my favorite part: providing students full access to AI Health Uncut. I receive a lot of messages daily, and I always strive to respond promptly to every one of them. Most are incredibly kind and supportive.

  I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it, as I always do.

  

  This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member of My Substack

• If you’d like to support my mission to uncover the brutal truth in healthtech and healthcare AI, please consider becoming a Founding Member of the AI Health Uncut community. You can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

All right, housekeeping’s done. Now back to AI upcoding…

AI is making medical upcoding easier, faster, and vastly more scalable. At its core, upcoding means billing for diagnoses, conditions, or care that were not actually documented or delivered. That is fraud.

The laws are already on the books. The problem is enforcement. “Maximizing billing and coding” has become one of the fastest-growing businesses in American healthcare, and AI is transforming what was once a manual compliance problem into an industry of its own.

I examine some of the most recent AI-related upcoding cases, then lay out the red flags to watch as a new generation of “autonomous” medical coding companies races into the market.

I also connect this AI upcoding saga to a direct question I recently put to Heidi Health CEO Tom Kelly on “Digital Health Inside Out.”

• Last Wednesday, August 26, the DOJ announced a $541.5M False Claims Act settlement with The Villages Health System, a Florida primary care group, over invalid Medicare Advantage diagnosis codes submitted to Humana, UnitedHealthcare, and Florida Blue. DOJ dates the conduct “from 2020 through 2024,” and the settlement agreement pins it to January 2020 through December 2024. A bankruptcy judge approved it on August 25. It is the second-largest Medicare Advantage settlement I know of, arriving seven months after Kaiser’s $556M. It’s mind-blowing how healthcare organizations, big and small, can cough up more than half a billion dollars just to make the bad news disappear. 🤯

• The mechanics are the story. Per the settlement agreement, employees ran two internal programs called “Retrospective Amendments” and “Sprints,” inserting diagnosis codes into charts “sometimes months to over a year after the patient visit,” not initiated by the rendering provider, and routed for approval to a non-rendering provider such as the chief medical officer when the original doctor had left. The health system’s own consultant sampled 100 beneficiaries a year and found unsupported codes rising from 28.6% in 2020 to 50.7% in 2024.

• Here is the part every healthcare AI CEO should read twice. During that same window, The Villages Health publicly celebrated an AI platform from KAID Health that mined its records for “care and coding gaps,” and Villages’ own chief medical officer, Dr. Jeffrey Lowenkron, co-authored a case study reporting $2.5M in “net new revenue” and a “10-fold return on investment.” That case study is still on KAID’s homepage today, August 31, 2026:

  

• To be clear: DOJ does not name KAID or any technology vendor. The settlement agreement contains no mention of software, algorithms, AI, or a third party. There is no public evidence that KAID generated, selected, or transmitted a single disputed code. I am not accusing KAID of anything. I am pointing at the overlap and saying it demands reporting.

• The same day, Arintra raised $25M, claiming $51M total (Dealroom and CB Insights count about $46M), to autonomously code every chart with, per its product page, “Zero human touch” and “Zero coding errors.” Its own blog says 10% to 20% of charts go to a human. Its Bengaluru job ad says the goal is to “maximize revenue for healthcare providers.”

• On August 12, I asked Heidi CEO Tom Kelly on our podcast where the line is. His answer, in full below, is the most honest thing a vendor has said on this: never fabricate, and the only real fix is scribes and payers working from one shared record. Otherwise, in his words, “everyone will have to use something like Heidi or Abridge to be claiming and upcoding.”

• Also inside: 11 red flags at Arintra, my 12 questions for the company, 5 for KAID, and the prediction I will be held to.

🚨 TL;DR

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

It’s time for some seriously spicy healthtech news from August. 🌶️🔥 And wow, did this month deliver.

We had Epic basically going, “Yep, I’ll take all of healthcare, thanks.” 😏 Then came yet another group of investors confidently telling us that AI is about to replace doctors. You know, basically tomorrow. 🤖🩺 And, naturally, Commure managed to f*ck things up again, this time winding down its referral program around the time of a STAT investigation. 🍿

So yes, there’s a lot to unpack.

But let’s start at the beginning. 👇

On August 20, the final day of Epic’s UGM, we recorded the August “Digital Health Vitals” episode of Digital Health Inside Out in person with my dear friends and colleagues Alex Koshykov, Stephanie Davis, and Bailey Morgan. 🎙️ Alex Koshykov hosted. Stephanie Davis, who spent pretty much her entire career covering health tech on Wall Street, brought the Q2 earnings scorecard. Bailey Morgan brought an operator’s view from a large pain practice on Long Island, plus a past life at Athelas, now known as Commure.

As for me, I mostly sat back, listened, and learned. Well, mostly. 😉

The full episode is here:

Three stories matter most. Health Catalyst, a company that has never posted a GAAP profit, sold the unit that produced 60% of its adjusted EBITDA. Epic announced literally everything, including things that do not exist yet. 😉 And R1 bought the prior authorization company that Olive AI left for dead. Everything else, from Teladoc to Commure, is in the quick hits at the bottom.

TL;DR:

1. Health Catalyst sold 60% of its adjusted EBITDA to become debt-free

Stephanie’s two biggest losers of the Q2 season were Teladoc and Health Catalyst. Teladoc I expected. Health Catalyst: not really. I said on air that they are one of the good guys, and Stephanie’s reply was the line of the episode: “They’re a great product, but their stock wasn’t one of the good guys.”

The HCAT stock bottomed at $0.96 on April 10, or $73M in market cap. On June 4, Health Catalyst announced the sale of Vitalware to Med-Metrix for $147M in cash, more than the company’s entire market cap at the time! The shares jumped 46% in a day, and by the eve of the Q2 print the stock had more than doubled off the April low. Heroic, until you read the fine print. 😉

On August 6, the company cut 2026 revenue guidance to $246M to $249M from the $260M to $265M it guided in May, and cut adjusted EBITDA guidance to $18M to $18.5M from $30M to $33M. Vitalware had contributed $11.4M of the $19.1M in first-half adjusted EBITDA. They sold roughly 60% of their adjusted EBITDA, and adjusted EBITDA is the generous yardstick. Health Catalyst has never(!) reported a GAAP net profit, and it is not about to start. Q3 adjusted EBITDA guidance is $0 to $0.5M. The stock fell 25% the next day, and Canaccord downgraded it to Hold on August 20.

The proceeds repaid the credit facility in full, so Health Catalyst is now debt-free.

That is survival, not a growth story.

What is left is a healthcare data company with under $250M in revenue, a second half guided to roughly break-even adjusted EBITDA, a $122.5M goodwill impairment already booked this year, and a $118M market cap. Private rival Innovaccer last priced at $3.45B, roughly 29 times that.

Stephanie’s verdict: they need to change the narrative, and she does not know how they dig out of it. Neither do I.

2. Epic UGM 2026: promises, partners, lawsuits, and a cowgirl hat 🤠

Judy Faulkner gave her executive address on August 18 dressed as a cowgirl, in keeping with the county-fair theme “Meet Me at the Midway.” The list was enormous: Ergo, Epic’s new “healthcare intelligence” layer built around its Art charting assistant (Ergo is Latin for “therefore”), an Agent Factory with about 120 agent features live, real-time prior authorization checks, and EpicOps, Epic’s new enterprise resource planning (ERP). My problem is the same every year: nobody knows what is real and what is “in the works.” To be fair, Judy’s own scorecard says 84 of the 167 projects promised at UGM 2025 shipped on time.

Epic is going down market. Organizations that cannot get Community Connect are now offered Sonnet, Orchard and Garden Plot, Garden Plot is being extended toward smaller hospitals, and a “Flower Pot” tier for very small practices is in development. That is eClinicalWorks and athenahealth territory, and Epic already holds 43.7% of the U.S. acute care EHR market. Bailey’s read was the most honest in the room: small practices are getting rolled up by private equity, and being on Epic makes a practice easier to integrate and “sexier” to sell. Good for the industry, maybe. Bad for patients. Patients always pay.

Abridge and Epic now compete at Epic’s own conference. On August 17, the first morning of UGM, Abridge made its clinical decision support agent available to every clinician at its partner health systems, including clinicians who do not use the Abridge scribe. The next day Epic unveiled Ergo. Both lean on Wolters Kluwer’s UpToDate for evidence. Neither mentioned OpenEvidence, which after the Nature Medicine paper (my take here) and the endless Doximity litigation looks more and more like the kid nobody wants in the sandbox. When we interviewed Shiv Rao on this show in April, his answer on FDA clearance was that a scribe transcribes and assigns codes, so there is nothing to clear. Once the product starts interpreting results, that answer stops working, as I argued in “Mayo Clinic + Abridge: Not FDA Approved!“ Also absent: Microsoft, whose Dragon technology was supposed to power Epic’s scribe a year ago. With Judy, nothing is exclusive. I wrote a year ago that Epic picked the wrong horse in the AI scribe race.

Real-time prior auth is a “no” delivered faster. Epic’s prior authorization check went live at four health systems with UnitedHealthcare, Aetna and Network Health, five months ahead of the January 1, 2027 payer API deadline under CMS-0057-F. Bailey, who lives inside payer rules every day, shrugged: any practice that has worked with payers long enough already knows which CPT codes need an auth. My concern is different. If the denial rate stays the same, or goes up, I do not care that the “no” arrives in real time. What Epic bought with this launch is a seat at the table in Washington as the compliant one.

The monopoly debate. Stephanie’s counter to my monopoly complaint was Apple: similar dominance, and nobody says Apple is bad for phones. My answer is that Apple innovates because regulators and Android breathe down its neck, while many of Epic’s “features” are partner apps under an Epic label. Until there is real pressure, Epic will keep maximizing revenue with minimum effort. And here is what I underplayed on air: three days before UGM, Reuters reported the FTC is probing Epic over data access, STAT reported federal and state investigators are looking at its NDAs, the Texas attorney general sued in December, and the Particle Health antitrust case is in discovery.

That’s probably more regulatory and legal pressure than Epic has faced in its 47-year history. And yet, it still doesn’t seem like much. Judy didn’t even break a sweat in her cowgirl outfit. 🤠

3. Cosmos is the one Epic product I am rooting for

I have to be positive sometimes. 😉 Stephanie pounced: so now I am positive on something that owns most of the market? 😉 Yes. Cosmos, Epic’s de-identified research dataset, now covers 320M patients and 23B encounters and has produced more than 220 peer-reviewed papers. Curiosity, the generative model trained inside Cosmos to simulate patient trajectories, is being validated at 20 organizations ahead of general availability in March 2027. And Epic Research Health Alerts, launched in April, publish county-level warnings when diagnosis rates spike. As I write this, the board shows cyclosporiasis across 19 states and measles in Pennsylvania.

That last piece is my hope. Epic sees patients walk into clinics in near real time across half the country, no names required. If Cosmos can flag an outbreak in a county a week before the CDC does, that is public health infrastructure the government never managed to build. Nobody has proven it at scale yet. On paper, Epic has it. Prove it, Judy, and I will say something nice about Epic twice in one year. 😉

4. R1 bought the prior auth company Olive AI left for dead

On August 18, in the middle of UGM, R1 announced it is acquiring Humata Health, terms undisclosed. It is R1’s second AI acquisition in ten months, after Phare Health, the London coding startup it bought in October 2025 and then named its entire operating system after. Both go into R37, the AI lab R1 launched with Palantir in March 2025. Stephanie’s framing: Ensemble got the credit as the RCM innovator because it never carried the burden of being public, while R1, historically an offshoring play with roughly 60% of its workforce outside the U.S., went private in an $8.9B deal in 2024 and is now building an AI-native revenue cycle on both the coding and the claims side.

Bailey didn’t hesitate for a second to say on air that she’s bullish on R1. Part of it is the Humata story itself. Jeremy Friese sold Verata Health to Olive AI for about $120M in December 2020. When Olive collapsed on October 31, 2023, the day I published “The Death of Olive AI,” Friese bought Olive’s prior authorization unit back, rebuilt it as Humata, raised $25M in 2024, and has now sold it a second time. You have to give it to this guy. He is quite a trader. Meanwhile, prior auth in America still runs on fax machines. 📠

Bailey drew the distinction that matters. Epic’s new product is an authorization check. Humata obtains the authorization and tracks it to the end, which is the more valuable of the two. Stephanie added the detail everyone forgets: R1 is largely a Cerner shop, with Ascension, its largest customer at about 40% of revenue, running Oracle Health, while Epic shops lean Ensemble, which says 86% of its clients run Epic. Different user bases, different fights. And Ensemble took a Thoreau investment in June at a reported $12B valuation.

Revenue cycle is where the money in healthcare AI is being made right now. Not the scribes.

5. Quick August healthtech hits

• Teladoc fell 28% on July 30 after cutting BetterHelp’s growth outlook by about 12 points and guiding full-year revenue down about 5%. Cash-pay therapy is evaporating because insurers now cover it, and Teladoc does not have enough in-network therapists to catch the demand. Stephanie: “I don’t know how they fix it.” I wrote in January 2025 to expect massive public bankruptcies in digital health. Nothing here changes my view of where the Livongo hangover ends.

• Doximity closed up 33% on August 7 and nearly doubled intraday. Revenue guidance went up, EBITDA guidance came down on AI spending, and with 19.7% of the float sold short, the squeeze did the rest. Alex, of course, credited Amit Phull’s appearance on our podcast in July. 😉 The OpenEvidence-eats-Doximity panic I dissected on Doximity’s worst stock market day in February looks weaker every quarter. Doximity is profitable, which still counts for something in a sector full of AI companies that are not.

• Amwell jumped 25% on the print. I wrote “COVID Finally Killed Amwell“ in July 2024. The patient is not only alive, it is beating and raising under CFO and COO Mark Hirschhorn, the former Teladoc CFO, with a Defense Health Agency intent to award in hand. Stephanie says management teams change stories. I asked where the moat is when every local provider offers telehealth. Her answer, government contracts and subscription revenue, is at least a real answer. I don’t know, Stephanie. Time will tell. 😉

• Oracle Health bracketed UGM with a patient portal on August 12 and an expanded Clinical AI Agent on August 19, both timed to look busy. Bailey’s Oracle demo last December was the first she had ever sat through where the vendor said, “We actually don’t have this yet, but we’re going to be selling it.” Oracle cut 21,000 jobs in fiscal 2026, including more than 500 in Kansas City, where Alex had just spent the day with former Cerner employees who have no idea where the product is going. The VA went live at three more hospitals in Indiana this week, 11 for the year, with every VA facility promised by 2031. Stephanie’s bet: Oracle spins or sells Cerner within five years, once the tax clock allows it. If you are Oracle, do you spend your time on OpenAI or on Cerner?

• Commure. STAT’s August 12 investigation documented a referral program under which one physical therapy clinic had to send Commure $750K in new business within a year or pay a $66K “referral obligation fee.” Commure had already sent customers a notice, dated August 6, that the payment agreements would be terminated, which STAT reported the day after the investigation ran. I published “Commure’s Long History of Red Flags“ on August 13. My original investigation was withdrawn under legal threats from Commure, so forgive me if I enjoyed reading STAT’s version. Stephanie’s “devil’s advocate” defense: partnership fees are old news, and One Medical booked 42% of its 2020 revenue from health network partners before Amazon bought it. Bailey, who was at Athelas when it asked her to sell an EHR that was not finished, thinks Commure is being made an example of in an industry that runs on relationships. My view has not changed. When your customer is also your investor, the investor wants a higher price and the customer accepts it. That is a conflict of interest, however common. And Stephanie’s advice to founders is free: do not fight with journalists. Work with them.

  

• Hippocratic AI. We ran out of time for the other General Catalyst company on air, so here is the print version. On August 13, the day after the STAT piece on Commure, Hippocratic AI announced “orchestrators”, which it defines as teams of voice agents run by a “supervising orchestration brain,” and repeated its favorite statistics: “99.89% correct advice,” validated by its own count across 775,000 calls by more than 7,700 U.S.-licensed clinicians, plus 250M patient interactions with “zero incidents of serious harm.” Numbers a company produces about itself remain numbers a company produces about itself, a point I made in “OpenEvidence Goes Hippocratic AI.” The company raised $126M at a $3.5B valuation in November for acquisitions and international expansion, which reads differently now that (reportedly) the international sales team has since been “restructured.” “Point solutions will only get you so far,” says CEO Munjal Shah. I agree, just not in the way he means. In any case, even if we take Hippocratic’s own press-release numbers at face value, it now has Epic breathing down its neck. At UGM, Epic announced front-desk voice AI as part of its broader push downmarket.

  

Watch the full episode here:

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then—go tell your friends!

👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

TL;DR:

• On August 12, my co-host Alex Koshykov and I interviewed Tom Kelly, co-founder and CEO of Heidi Health, and Simon Kos, Heidi’s Global Chief Medical Officer and formerly Microsoft’s Chief Medical Officer, on Digital Health Inside Out.

• Kelly’s most surprising claim:

  Frontier models stopped getting better on Heidi’s core tasks somewhere around Claude Sonnet 3.5. After that, new releases were “noise” on their tasks, and the models “get different, not better.” So Heidi went back to building its own models, and claims the result is both cheaper and better.

• That is the mirror image of the argument I made in June in “OpenEvidence Goes Hippocratic AI”: the Nature Medicine study showed frontier models beating specialized clinical AI on every benchmark. I’ll show you where the two claims collide, where they quietly agree, and why the real disagreement is about data moats and economics, not intelligence.

• Kelly also cited an unpublished randomized trial claiming only 1 in 10 doctors catches a dangerous hallucination in AI notes, predicted 90% of AI scribes will die, and said “yes, for sure” to becoming the first FDA-regulated AI scribe.

• In June 2025 I gave Heidi a neutral rating in my “Top 50 AI Scribes: The Brutal Truth“ ranking (verbose notes, editing burden, weak integration, no independent validation). Inside: what aged well, what I got wrong, and a rapid-fire tour of the rest of the episode.

We finally got Heidi on the show

Quick context. Heidi Health is the Melbourne-born company that grew into one of the most widely used AI scribes on the planet. Kelly is a former vascular surgery resident with a computer science and math background who started building clinical tools in 2019 and first raised for Heidi in 2021. Kos spent years at InterSystems and Cerner, then 15 years at Microsoft, before joining Heidi in October 2025, the same month Heidi announced a $65M Series B led by Steve Cohen’s Point72 at a $465M valuation. The volume claims keep climbing: roughly a million visits a week in early 2025, 2 million at the Series B, and on our show Kelly casually said 3 million.

One fun fact. Heidi began as a history-taking tool that suggested a diagnosis. History into diagnosis: “Hi to Di.” Everyone heard “high to die,” nobody could spell it, and Heidi was born. 😉 Meanwhile, the YouTube transcription bot that processed our own episode renamed me “Sergey Politico” and the show “Digital Halt Inside Out.” I choose to read that as supporting evidence for everything that follows. 😂

Tom Kelly’s claim: the frontier stopped improving on Heidi’s tasks

When GPT-3.5 landed, Kelly and his co-founder Yu Liu had the same reaction at the same time: “Oh no, we’re probably cooked.” In 2023, Heidi stopped trying to out-model the labs and bet on the application layer. Then early 2025 brought scale, and with it a lottery: “I’ve sent the same request one second apart and it’s literally like toddler and PhD.” Kelly even claims performance sags with API load, accusing providers of quietly quantizing models during busy hours. But his core claim was bigger:

And the kicker:

Every builder in this space knows that feeling: a doctor spends hours tuning templates, and a smarter model that improvises is a worse model for that doctor.

So Heidi went back to its deep-tech roots and started fine-tuning its own models. The constraint makes it interesting: no training on patient-identifiable data, and outside the US not even on de-identified data. So they generate synthetic transcripts with frontier models as supervisors, then reinforce against real clinician preference signal (pick output A or B) at 3 million visits a week. Kelly’s conclusion: “We actually just get a data set that the frontiers don’t have,” and the result is “both cheaper and better on our tasks.” The cheaper part comes with numbers: speech-to-text on their own rented hardware is, he says, 100 times cheaper than APIs, and running it through Gemini Flash would cost 2 to 3 million dollars a month while hallucinating more medication names. The strategic logic, verbatim: “They’ll always put 40% to 50% gross margin on cloud, and that’s our margin to eat.”

Two months ago I argued the exact opposite

Now you understand why I was squirming in my chair, in the good way.

On June 12, Nature Medicine published the NYU Langone study showing general-purpose frontier models beating OpenEvidence and UpToDate Expert AI on all three benchmarks, with the specialized tools performing no better than Google’s free AI Overview on real physician queries. I covered it in “OpenEvidence Goes Hippocratic AI,” and I’d been making the underlying argument since “Painful AI Adoption in Medicine“ in August 2024:

People laughed at me in 2024. Nobody was laughing in June.

So when the founder of one of the fastest-growing clinical AI companies, a man in the trenches betting his company on the claim, tells me the frontier stopped getting better on his tasks, I have to take it seriously.

Who is right? We are answering different questions.

The Nature Medicine study measured medical knowledge and answer quality, and on that axis Kelly barely disputes the frontier’s win. His concession on air: “I can only imagine how many clinical questions are landing in Claude and ChatGPT. So they actually do have pretty good products on that task.”

Kelly’s plateau (the word is mine, from my question on air, his words were “stopped getting better” and “noise”) is about a different axis: production reliability on a narrow task. Same transcript, same template, ten thousand times a day, no invented drug names, at survivable cost. On that axis, he argues, raw intelligence stopped being the bottleneck around Claude Sonnet 3.5. What matters now is consistency, rule-following, latency, and cost. Framed that way, both stories are true simultaneously.

The frontier keeps getting smarter, but it stopped getting better for Heidi’s tasks.

The remaining disagreement is defensibility. Kelly’s moat has three legs:

• preference data the labs can’t touch (their enterprise agreements prohibit training on customer data),

• regulation (in the UK, Heidi is a regulated medical device, so it’s not as simple as “API endpoint, Claude Fable, build me an AI scribe, thanks, I’m done”), and

• economics (hyperscalers won’t undercut their own cloud margins, “it’s never a can’t, it’s a focus issue”).

My counter, which I said a version of on air: every leg is rented. Focus changes with one board meeting, and the day Big AI goes all in on clinical, the moat evaporates faster than a Twitter thread. To his credit, Kelly didn’t pretend otherwise:

“If they give us a better, cheaper model that meets our standards and is consistent, we would use it. [...] Maybe Fable 7 distilled is incredible at everything and it’s no longer required to do this. I’m not sure.”

That’s more honesty than OpenEvidence has produced on the record, ever. 😉

The safety numbers nobody publishes

Kelly says Heidi ran a randomized trial, not yet published, showing AI scribes introduce errors doctors overwhelmingly fail to catch: “One in 10 doctors will find a dangerous hallucination in notes, and average models create a large number of these dangerous hallucinations.” He described Heidi’s own worst safety incident: a hallucinated chemotherapy drug name, nearly identical to the real one, wrong molecule, plausible dose, invisible to a tired reviewer. The published literature (NOHARM, the UCSF emergency department study) points the same direction.

Which sets up his most quotable prediction: “90% of AI scribes will die.” The survivors, he says, will be the ones that prove consistent performance on safety benchmarks. For the record, I counted 126 AI scribes over a year ago, most of them copycats. If Kelly is right, roughly 113 are walking dead. I won’t pretend to be sad, because I’ve written ad nauseam about the "AI tourist" / copycat problem in clinical AI.

Skeptics would point out that Alex Lebrun, my friend and the CEO of another leading AI scribe company, Nabla, made similar prediction back in January 2025. So far, the jury is still out… That’s the funny thing about forecasting. You can eventually be proven right, but only after everyone who heard the prediction is dead. 😉

When I asked whether Heidi would become the first FDA-regulated AI scribe in the US, Kelly didn’t hedge: “Yes, for sure,” followed by the line I keep thinking about: “It just freaks me out how little the US systems care about safety.” A vendor asking for more regulation of his own category is either very confident or very good at marketing.

My June 2025 Heidi scorecard, revisited

Time to eat my vegetables. In Part 4 of “Top 50 AI Scribes: The Brutal Truth“ (June 2025), I gave Heidi a neutral rating. My reasons: middling note quality with drafts clinicians called way too verbose, a real editing burden, subpar integration (a standalone app with copy-paste into the EHR), and no independent validation. I credited competitive pricing, broad specialty fit, privacy posture, and building in-house instead of being an AI tourist.

Fourteen months later, the audit of my audit.

What aged well: the validation critique stands fully. The impact reports are self-published, the randomized trial is unpublished, and Kelly himself concedes that on structured, problem-based charting benchmarks Heidi is “maybe not quite as good,” his defense being that a scribe welded to the record is “just a feature of the EHR” and not the game Heidi wants to win.

What aged badly: the integration critique needs a major update, with Heidi now live on Epic via SMART on FHIR at Beth Israel Lahey Health and Franciscan Alliance, on the athenahealth Marketplace and eClinicalWorks via Vim, plus a revenue cycle partnership with R1. And one correction: my table listed Heidi as bootstrapped. Wrong then (Blackbird), wrong now (Point72). I stand corrected. 😉

What changed underneath me: per Computer Weekly, more than 80% of Heidi’s workloads now run on their own models. The product I rated and the product shipping today are not the same product, which is exactly why every scorecard in this market has a shelf life measured in months.

Will Heidi submit to an independent blind bake-off, the way Cleveland Clinic ran one across five scribes before choosing Ambience? We ran out of time before I could ask. Tom, Simon, consider it a standing invitation.

Everything else from the episode, in brief

We covered far more than one thesis in 66 minutes. The rapid-fire version:

• The mission. Heidi’s stated goal is to “double the world’s healthcare capacity,” and Kos says that vision, not the scribe, is what pulled him from Microsoft. Kelly’s link from notes to capacity: more than half of weekly active users now ask clinical questions inside Heidi, so evidence gets applied in the room, and every saved hour either sees another patient or keeps a doctor from quitting.

• The time-savings evidence. Heidi’s own impact reports across Australia and New Zealand, the UK, and Canada claim 59 minutes returned per 8-hour shift, and in New Zealand, where every hospital with an emergency department runs Heidi, an extra patient seen per ED shift. Kos also framed unpaid documentation overtime as a legal risk, citing class actions by doctors. Patient acceptance of recording, they say, exceeds 99%. All self-reported, so trust accordingly.

• Two kinds of scribes. Kelly’s taxonomy: EHR-welded form fillers (”not useful, just a feature of the EHR”) versus open-ended AI surfaces where the real US value lives, in denial responses, claims, and patient-specific summaries. Heidi never built the former, which conveniently explains why it benchmarks poorly on it.

• The billing elephant. I brought up Texas Oncology going from 3.0 to 4.1 billed diagnosis codes per patient with ambient AI and Riverside’s 14% jump in documented HCCs. Shiv Rao of Abridge told us on this show that scribes are becoming billing tools, and Kelly’s response was blunt: “I think Shiv’s saying that because he has to say that.” His version: doctors chronically under-document (his orthopedic surgeons wrote “day 3, total knee” no matter what happened to the patient), capturing reality is the job, fabrication is the red line, and the endgame is scribes and payers working from one shared transcript instead of an AI-versus-AI upcoding and denial arms race that raises costs on both sides. Hence his interest in the R1 partnership. I remain more cynical about US incentives, and said so.

• The public option aside. Asked about fixing those incentives, Kelly, an Australian, said a government-funded alternative creates a counterbalance that pure private markets can’t: in an emergency “you just have to go to the nearest hospital,” and in an all-private system “you’re bankrupt and you’re in trouble.” He doubts the US will ever do it.

• The Epic-doom exception. When Christina Farr came on our show, she called Heidi the one smart exception to her thesis that Epic dooms US health AI startups. Kelly confirmed it was half strategy, half luck: after a 2022 HLTH trip he concluded Heidi could never out-raise the US land grab, so they went free, horizontal (dentists, vets, hospitals), and Commonwealth-first, then followed organic US adoption instead of buying it. The ambition now is to be the chosen AI productivity layer for doctors, closer to OpenEvidence or Doximity than to an Epic checkbox. Systems that want the record filler won’t pick Heidi, “and that’s fine.”

• Kos’s two busted myths. Fifteen years in health IT taught him that everything must ship through the EHR and that product-led growth can’t work in healthcare. Heidi, he says, broke both. His frame: the EHR remains the system of record, but an AI “system of work” is forming on top, like a slick banking app running against a COBOL mainframe. In the US, where IT decisions are top-down and policy violations have teeth, the scribe gets flattened into an EHR feature, which is why Heidi’s US play needs enterprise sales muscle on top of viral adoption.

• Stickiness and the free tier. Three sessions in, Kelly claims roughly 60% long-term retention, powered by “a million little details” like preserving your manual edits when you switch templates. About 100,000 clinicians use Heidi free every week, which he calls “almost like our marketing budget,” funded by owning the models. His analogy: why does Granola exist when every tool has transcription? “It’s just better.”

• Sovereignty. A quiet reason for in-house models: countries like New Zealand want clinical AI running in their own cloud, and frontier APIs at those volumes get “super expensive, like a dollar a query.”

• The economics tease. No revenue disclosure, but Kelly claims the business works ex-growth and quoted an investor calling Heidi’s economics “the best they’ve seen in the industry.”

• Hardware and a jab at Abridge. Heidi now ships its own microphone, paid for now at subscale, possibly bundled later. When Alex noted Abridge’s foundation-model partnership with NVIDIA, the deadpan came back instantly: “It’s fake news. Partnerships aren’t real.” A joke. Mostly.

• Why Heidi’s social media is actually funny. Kelly credits the Australian instinct to not take things too seriously and a deliberate wink at the cutting dark humor that gets clinicians through the day. In a commoditized market, he argues, people pick the vendor they like being around.

• The roadmap. Less about the next product, more about renting out capability: 150 of “some of the world’s best AI engineers” doing co-development and new IP with enterprises that can’t hire that talent themselves. Scribe, Evidence, and comms are capabilities, and the enterprise motion is “help us solve this gnarly problem.”

My verdict

The Nature Medicine result and the Kelly plateau are not opposites. Frontier models are winning the knowledge game, which is the game OpenEvidence sells, and that moat is gone, as I wrote in June. The production game (consistency, template obedience, drug-name accuracy, regulatory error bars, unit economics at 3 million visits a week) is a different sport, and Kelly makes the strongest case I’ve heard that it currently favors the specialist. Notice the word currently. His own hedge says it best: the moment a frontier lab ships a cheaper, consistent model that meets his standards, he’ll use it. The moat isn’t the model. It’s the preference data, the regulatory scaffolding, and the willingness to be audited.

So publish that trial, publish the impact report methodology, and walk into an independent bake-off with cameras rolling.

A founder who says “90% of AI scribes will die” is implicitly claiming a seat among the survivors. In June 2025 I wasn’t ready to grant Heidi that seat. In August 2026, they’ve earned a strong maybe, which, from me, is practically a love letter. 😉 We had roughly 25 more questions we never got to, including data retention defaults and what multiple Point72 actually paid. Tom and Simon promised a round two in a year. I intend to collect.

Watch the full episode here:

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then—go tell your friends!

👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Disclaimer: The opinions expressed here are solely my own and do not represent those of any sponsors, advertisers, or supervisors. Oh, wait. I don’t have any. 😂

But first, a few personal notes:

• I spent this summer in Lithuania, helping my ailing parents while also trying to build a healthtech community in this amazing, tech-talented country. Navigating the healthcare system in an EU country like Lithuania as a son, rather than as an analyst, is a humbling experience. It taught me more about the real state of healthcare than any benchmark ever could. Thank you to everyone who kept reading, writing, and checking in while I was mostly away from the keyboard. It meant more than you know. 🙏

• I’m honored to be speaking at the Origin Point healthcare technology conference in beautiful La Jolla, CA, on September 16–18, 2026. If you’re there, please come say hello!

• And my favorite part of the summer: providing students full access to AI Health Uncut. I receive a lot of messages daily, and I always strive to respond promptly to every one of them. Most are incredibly kind and supportive.

  I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it, as I always do.

  

  This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

  Become a Founding Member of My Substack

  If you’d like to support my mission to uncover the brutal truth in healthtech and healthcare AI, please consider becoming a Founding Member of the AI Health Uncut community. You can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

All right, housekeeping done. Now for today’s main event.

Well, well, well. The “AI will replace doctors” debate, which, as my paid subscribers know, I’ve been chronicling since Warner Slack’s automated patient questionnaire in 1955, just got its most prestigious endorsement yet.

Yesterday, August 17, 2026, JAMA published a Perspective titled “Will Autonomous AI Exceed AI-Aided Physicians as the Best Medical Care?” (Translation: not a study. An opinion piece.) The authors answer their own question with two words: “We disagree.” Not with the premise, but with the medical establishment. The AMA, the American College of Physicians, and Robert Wachter’s new book all conclude AI should assist doctors. The authors say AI alone is “likely to be better” than doctors, and better than doctors using AI, at 5 fundamental cognitive medical tasks. And they say the switch could be ready “in some, maybe many, workflows by 2030.”

Who are these bold authors? Ezekiel J. “Zeke” Emanuel, oncologist, bioethicist, architect of the ACA (aka Obamacare), who in 2016 added a second career as venture partner at a multibillion dollar healthcare investment fund (we’ll get to that). Abe Baker-Butler, Emanuel’s research fellow at Penn and co-first author. Neal Khosla, CEO of Curai Health, an autonomous-AI primary care company. And Vinod Khosla, Neal’s father, billionaire founder of Khosla Ventures, early OpenAI investor, and the man who has been predicting the end of doctors since 2012 with the consistency of a Swiss watch and the accuracy of a Magic 8 Ball. 🎱

My longtime paid subscribers know where I stand, because I laid it out across my 6-part series on this exact debate. I am not a doctor-protectionist. In Part 4, I made what might be the most comprehensive case yet FOR the “AI will replace doctors” argument, and I ended it with “Wake up, America. It’s time to embrace AI in healthcare.” I meant every word. I have also publicly predicted that fully autonomous AI diagnosis, with no human in the loop, is coming.

So no, I’m not here to tell you the Khoslas are wrong about the destination.

I’m here to tell you that this JAMA article is not the vehicle that gets us there. It’s a pitch deck wearing a lab coat.

TL;DR:

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist and constitutes journalism, commentary, and opinion protected by the First Amendment to the U.S. Constitution and other applicable principles of freedom of speech and freedom of the press. Those protections do not eliminate the responsibility to report accurately, and I take that responsibility seriously. I endeavor to base factual statements on verifiable information, primary-source documentation, publicly available records, interviews, and other evidentiary materials. Where I express opinions, interpretations, inferences, or conclusions, they should be understood as such and are based on the information available to me at the time of publication.

I have no financial interest, whether long or short, direct or indirect, in any of the companies discussed in this article. I have not received compensation from any person or entity for publishing this article. Nothing in this publication constitutes financial, investment, legal, or other professional advice.

I previously contacted Commure and provided the company with an opportunity to comment in connection with my prior investigation. I did not separately contact Commure for this article because this publication is intended primarily to summarize, discuss, and comment on previously published reporting, research, and other publicly available information, rather than to present new original allegations concerning the company.

Any factual errors brought to my attention will be reviewed and, where appropriate, corrected or clarified.

With the brand-new investigation by STAT News published yesterday, let’s review the long history of red flags at , which goes back much further than the issues mentioned in the STAT article.

🚨 But first, a quick announcement:

As you are well aware, these research and investigations are time- and labor-intensive. I’m a one-man operation. My Founding Members Club is what I heavily rely on for resources to conduct these kinds of thorough investigations. If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. (Substack may ask you to enter your email first before taking you to the payment page.) You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Become a Founding Member

🚨 Another quick announcement:

I’m honored to be speaking at the Origin Point healthcare technology conference in beautiful La Jolla, CA, on September 16–18, 2026. If you’re there, please come say hello!

Alright, back to Commure…

I’m happy to report that both authors, Casey Ross and Brittany Trang, are my subscribers and are certainly familiar with my original Commure investigation, “The Many Faces of Commure,” published on March 9 of this year. The original version was published on November 30, 2025, but I withdrew it following legal threats from .

The industry, of course, has known about the upcoming STAT News investigation into Commure since at least May, when Athelas’s VP of Sales and Director of Partnerships spilled the tea. For the past three months, he has apparently been on something of a “pre-apology” tour, frantically calling Athelas clients to warn them that negative press was coming and assuring them that it was all untrue.

I’m, in turn, pissed at Commure. In its communications with me, the company repeatedly claimed that some of the facts in my investigation were not true. Now I’m looking at the STAT News report, and some of the exact same facts about Commure that appeared in my article are now being reported by STAT News. I discuss those facts below.

Facts are facts. Commure can deny them. Commure has threatened me. Commure has threatened STAT News. But the facts remain the facts.

The healthcare industry deserves an explanation.

TL;DR:

So let’s review the long history of red flags at Commure:

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

On January 14, my subscriber asked a fair question in the comments: what about Apple Health? It already has FHIR-based EHR data plus Watch data plus everything else Apple hoovers up.

Here is what I answered, verbatim:

1. There is no LLM associated with Apple Health. So you’re limited to the features Apple offers right now, which is like 15 versus infinity. 🙂

2. You can’t easily upload your own medical files, like a pdf or doc.

The recent announcement about Gemini taking over Siri may be a sign that Google is collaborating with Apple on health and other initiatives. That potentially could be bigger than ChatGPT Health and Claude for Healthcare if they do it right.

Last week OpenAI launched Health in ChatGPT to every U.S. user. So let’s do the scorecard on my own call from six months ago. 😉

TL;DR:

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously.

I have no financial interest, whether long or short, in any of the companies mentioned in this article. This article is for informational and opinion purposes only and should not be construed as financial or investment advice. I have held every company mentioned here to the same test: published data, evidence of benefit, a real regulatory and reimbursement path, and independent proof rather than a press release.

Also, this is not the product of my research alone. On the ultrasound therapy side especially, I have been consulting with experts in the field.

Midjourney did not start the whole-body scan conversation, but it definitely made a dent in the past few weeks. Unfortunately, the economics have not been kind to companies built around that idea, and the history of failures is impressive. No one knows for sure what will work, and any innovation in healthcare is welcome. But there is a second half of the story that almost everyone is forgetting: potentially economically viable, focused ultrasound therapy. In the piece, I overview the market and explain why this just might be the next big thing in medical imaging.

This piece is a natural continuation of our recent Digital Health Vitals podcast episode, where Stephanie Davis, Alex Koshykov, Amit Phull, and I discussed Midjourney’s “Spa MRI” and other exciting healthtech topics.

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always strive to respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it, as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 17 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member of My Substack

If you’d like to support my mission to uncover the brutal truth in healthtech and healthcare AI, please consider becoming a Founding Member of the AI Health Uncut community. You can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

OK, now let’s get back to our exciting ultrasound discussion.

The neurosurgeon taking the Midjourney scan-spa apart on Instagram is Dr. Brian Walcott, a Harvard-trained cerebrovascular and stroke specialist, and he is not swinging at a straw man. He is right that sound cannot cross bone or air, which means the heart, the lungs, and the brain, the organs a person would most want checked, are exactly the ones ultrasound sees worst, and no software update repeals that. He is right that Midjourney’s own founder admits the AI is labeling outputs, not doing the imaging. And he is right that “cost approaching zero” describes the sound waves and nothing else, not the hardware, the years of FDA review, the facilities, the reads, or the liability. The critique lands the same blow from the money side, and lands it harder than he does: screen millions of healthy people across a dozen organ systems and you will light something up in almost everyone, and most of it is not an early catch. It is a harmless finding that drags a well person into biopsies, specialists, and fear.

And Walcott is not the only physician making this case. From the other side of medicine, dermatologist Dr. Matthew Zirwas ran the same numbers and landed in the same place: roughly 90% of asymptomatic full-body-MRI patients show an abnormality, about two-thirds of it incidental, and finding the rest does not move overall mortality. He reaches for the same Korean thyroid and German melanoma cases we walk through below. His sharpest line named the business underneath the medicine, that a patient talked into a repeat “scam (sorry, meant scan)” every three months is training Midjourney’s model for free, and he asked who is really coming out ahead. Hold onto Zirwas. He does not stay in this chair, and where he moves later is the most interesting turn in the whole debate.

The oldest joke in medicine is that the way to stay healthy is to stay out of hospitals. Underneath the joke is a real finding: look hard enough at a well person and you will find a shadow, and chasing that shadow costs time, money, and fear for something that would have come and gone on its own. On consumer whole-body screening, the argument is won. We are not going to relitigate it.

A Histotripsy System is a medical device that destroys targeted tissue using focused ultrasound, but unlike many ablation systems, it does not primarily use heat, radiation, needles, or incisions. The best-known commercial version is HistoSonics’ Edison® System.

Here is the half of the conversation that keeps getting skipped. The problem is not believing too much in screening. It is assuming screening is the whole point of taking ultrasound out of the hospital.

The part that matters is not a machine that scans a healthy body and guesses. It is a machine that treats a sick one. Imaging and therapy on the same device, in the same second.

Aimed at someone with no symptoms, every objection above still holds. Aimed at a patient with a diagnosed clot in the leg or the brain, those objections start to fall away.

TL;DR:

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