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The Rural Radiologist Who Already Exists

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By STEVEN GOODMAN

The future of rural healthcare won’t be determined by how much money is invested. It will be determined by how wisely that money is used.

That line gets nodded along to at every rural health conference, then quietly ignored the moment the conversation turns to solutions, because the solutions on offer are almost always capital solutions: build a wing, buy a scanner, recruit a specialist who will not, in fact, be recruited. I spent a career building infrastructure for GE Medical Systems, standing up systems for remote patient monitoring before “telehealth” was a word anyone used. The lesson that career taught me is one rural health policy still hasn’t absorbed: the expertise a small hospital needs is usually not missing. It’s just licensed in the wrong state.

The Equipment Isn’t the Bottleneck

Consider radiology, because it’s the clearest version of the problem. A critical access hospital in rural Wisconsin can afford a CT scanner. What it cannot afford, and cannot realistically recruit, is a subspecialist radiologist sitting in the building waiting to read scans that come in one or two at a time. That expertise exists — in large quantities, at hospitals and reading services in Chicago, Minneapolis, or Milwaukee, often sitting idle overnight.

The technology to connect the two has existed for two decades. A scan can move from a rural imaging suite to a subspecialist’s screen in seconds. What can’t move nearly as fast is the paperwork — and there’s more of it than most people outside a hospital credentialing office realize.

A radiologist licensed to practice in Illinois cannot legally read a scan for a patient in rural Wisconsin without a separate license, a separate credentialing process, and — even after clearing both — a payer-enrollment process that may not be worth the trouble.

Multiply that by every specialty a rural hospital can’t staff, and by every state line a patient’s data has to cross, and you get the actual shape of the rural healthcare gap. It isn’t a hospital-building problem. It’s a jurisdiction problem.

Three Fixes, None of Which Require a Capital Campaign

Fixing licensing and reimbursement rules would do more for rural healthcare than another round of capital funding

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You Say “AI,” I Hear “Organoid”

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By KIM BELLARD

I must admit, ever since I learned about, and wrote about (OI May Be the New AI), “organoid intelligence” over three years ago, I’ve been looking to do a follow-up. I mean, sure, AI is in a very exciting stage, but that stage no longer seems like the future; it seems more like the present, with implementation issues. It’s data centers, hacking, impacts on jobs, open weight versus closed, and so on.  It’s market share, IPOs, and AI’s role in driving the stock market. People should certainly pay attention to it, but AI is not quite the open field that it was just a few years ago.

Organoids, on the other hand, are not quite here yet. They may – or may not – be the future of AI, among other things. I always like to look ahead to the next thing more than the at-hand, so when I saw some cool developments with organoids, I didn’t want to miss my chance.

Making some news last week, researchers at Harvard reported that they’d kept lab grown human brain organoids alive for over five years, three times the previous record. Not only that, but the organoids seem to “retain a memory of the time spent in vitro,” recording the passage of time, as it were.

“We didn’t know how far the development and maturation of human brain tissue could occur outside the context of the normal brain inside the head,” said Paola Arlotta, Golub Family Professor of Stem Cell and Regenerative Biology and senior author of the new paper. “This work showed that it’s actually possible to not just have these organoids survive in culture, but also continue to change, develop, and mature over stretches of time that had never been reached before.”

The team observed the organoid cells over the years, and found that they “faithfully modeled” the ways that human brain cells develop, including DNA methylation, a process in which genes are turned on and off during development, and which serve as a form of “brain clock.” When older and younger organoid cells were combined in a single organoid, the older cells stuck to the developmental stage they had been at, which researchers concluded meant they “recorded the passage of time and retain a memory of the developmental steps already performed.”

“We were a little bit shocked by the results,” Professor Arlotta said. “I like to call this a ‘time warp’ of development — they skip ahead.”

It’s obviously hard, and often unethical, to study actual human brain cells, so the researchers believe the organoids offer opportunities for more insights into brain development, as well as for testing drugs or predicting disease progression.

OK, you might say, that’s all very interesting and some great lab work, but it’s hardly AI, now is it?

Try this: last week The Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), DayOne, a Singapore-headquartered global data center developer and operator, and Cortical Labs, a Melbourne-based biological computing startup, announced they were partnering to form a Biological Data Center Prototype. The Center uses Cortical Labs’ CL1 biological computing system to offer “practical, sustainability-aligned alternative to conventional silicon infrastructure in Singapore through wetware-based computing.”

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The Least-Informed Person in the Room

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By DAVID KIRK

My father called me from a hospital bed a few years ago. He had been admitted to a small hospital with an abdominal infection, and his surgeon had offered him an open operation. He wanted my clinical perspective. I knew that in his condition, he was unlikely to survive general anesthesia and the open procedure. I spoke to the surgeon myself and asked why he wouldn’t ask interventional radiology (IR) to drain the infection instead.

The surgeon told me that the hospital lacked the equipment required for IR, and without IR, the open operation was the best option on his menu. After hearing my assessment, he agreed and helped arrange the transfer to a hospital that could drain the infection.

I still think about how that single phone call may have saved my father’s life. His surgeon wasn’t negligent. He made the best decision based on the information in front of him. However, my father was able to put another option on the table because he knew a physician to call.

Most families don’t have a physician to consult when their doctor makes a recommendation. For generations, patients have only known what the person in the room with them chose to share, bounded by what their equipment and offerings could provide. Today’s patients, however, have artificial intelligence options in their pocket. A patient can now ask their preferred AI model, “Is surgery really my only option?” and get an answer that sounds confident and informed. Hungry for options, patients are asking these questions in enormous numbers.

In many ways, this is a welcome rebalance. It puts information in patients’ hands and allows them to play a more active role in their own healthcare, but it also opens the door to misinformation and distraction, and that can be life-threatening. Researchers at Mass General Brigham found that when given only a patient’s presenting symptoms, general-purpose AI chatbots failed to produce an accurate initial or differential diagnosis 80% of the time. In July, a Florida pastor sued OpenAI after ChatGPT provided him with “extremely dangerous medical recommendations” that delayed his treatment for pulmonary embolism. My primary care colleagues have shared how the presence of an AI-suggested diagnosis has transformed a 10-minute prescription refill into an hour-long conversation about something unrelated to why the patient came in that day.

Patients are also adopting AI much faster than their physicians. The West Health-Gallup Center on Healthcare in America reports that among US adults who use AI for health information or advice, 59% use AI tools to research on their own before visiting a doctor, and 56% use them to research after visiting a doctor. Physicians are using AI too, but for a different job. According to the AMA, more than 80% of physicians now use AI professionally, mostly for administrative work, yet only 13% are using it for diagnosis, the very thing their patients are asking it to do. The reality is that without AI support, most physicians are realistically only able to review 3% of a patient’s chart.

Now that patients have AI to help them advocate and question, the physician is at risk of becoming the least-informed person in the room.

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Glucose Data Wants to Be Ambient

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By IRAKLI KURTANIDZE

I have lived with type 1 diabetes for more than twelve years, and I check my glucose the way most people check the time: dozens of times a day, in glances that last a second or two. A continuous glucose monitor sits on my arm and produces a fresh number every few minutes. The sensor is a small miracle. What happens to that number after it leaves the sensor is where digital health still falls short.

The industry default is that CGM data lives in one place: the phone app. But life with a chronic condition does not happen inside one app. It happens in meetings, at a desk, in the kitchen, on the couch, in the car. Every time I have to stop what I am doing, find my phone, unlock it and open an app, a tiny tax is charged. The tax sounds trivial until you multiply it by fifty glances a day, every day, for the rest of your life. Friction quietly reduces how often people look, and looking is the whole point of continuous monitoring.

This leads to an uncomfortable inversion for anyone who builds consumer software: the best diabetes tools should minimize time in app, not maximize it. Engagement, session length, daily active minutes, the metrics most product teams are paid to grow, are exactly backwards for chronic disease. A person with diabetes does not want to engage with their glucose. They want to glance at it and get back to their life. The right KPI is glances made cheap, not minutes made long.

Patients understood this before the industry did. Around 2014 the Nightscout community, parents and engineers rallying under the hashtag #WeAreNotWaiting, rigged their own uploaders and cloud dashboards so a child’s glucose could be watched from a work laptop or a smartwatch. CGM in the Cloud was ambient glucose, built by volunteers, a decade ago. Officially sanctioned data access has improved since, but real-time access largely remains gated behind partner programs while public APIs stay retrospective, which is why open-source bridges still fill the gap today.

When our team built Sugar Sense, an app from a small Estonian health-tech company (Sugar Sense OÜ), we treated that history as the spec. The principle: meet the number where the person already is. So the same reading shows up on iPhone and Android, on an Apple Watch face, on the web, in a Windows system tray icon and a Mac menu bar icon next to the clock, in a browser extension, and through Alexa if you would rather ask the room. The app is listed in the Nightscout project’s community app list, and the core app is free. None of this is technically glamorous. Putting a number next to the system clock is boring engineering. It is also, for a person who glances fifty times a day, the difference between a tool and a burden.

Ambient matters twice over for the people who love us. A parent at work, a spouse in another city, a grandparent learning to help: for them, real-time following turns background anxiety into a two-second glance. We made family following free in our app because access to a loved one’s safety signal should not sit behind a premium gate. The caregiver is not an edge case in chronic disease. Half the time, the caregiver is the user.

None of this is unique to glucose. Blood pressure, heart rhythm, oxygen saturation: chronic disease generates numbers that people live alongside for decades. The lesson from the CGM world is that this data should behave less like a document you open and more like the weather: ambient, glanceable, and shareable with consent, on whatever screen happens to be nearby. Builders in health tech should count the glances they made cheap. Patients are already counting.

Irakli Kurtanidze is the founder of Sugar Sense, a cross-platform CGM companion app, and has lived with type 1 diabetes for more than 12 years.

THCB Gang Special — The Odyssey of Women’s Health: Journeying into the Midterms

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Audrey Mann Cronin takes over THCB today and has a THCB Gang special called “The Odyssey of Women’s Health: Journeying into the Midterms – Obstacles, Opportunities and What We Can Expect”

The panel is Liz Powell, an attorney and Founder of both G2G Consulting and Women’s Health Advocates, and former Capitol Hill staffer; Dr. Elizabeth Garner, CEO of Sena Therapeutics, former CSO for Ferring, and past President of the American Medical Women’s Association; and, Dr. Mitzi Krockover who founded both Women Centered and Femtech AZ, and is an investor serving as Managing Director of Golden Seeds

What’s going to be the impact of politics, policy and the mid-terms on Women’s Health? Hear from these advocates, as they reference this summer’s blockbuster!

The transcript (made by Youtube and Claude so blame AI for any mistakes!)

Audrey Mann Cronin, Communications Advisor, Healthcare and Technology

Hello to everyone, and welcome to a special episode of Matthew Holt’s THCB Gang. My name is Audrey Mann Cronin. I’m a communications advisor in healthcare and technology and a passionate women’s health advocate. I’m so pleased to be joined today by a powerhouse of women healthcare leaders. But before we start, a big thank you to Matthew Holt, who, in a rare tender moment, offered to have me be the Rosie O’Donnell to his Jimmy Kimmel.

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HealthEx — The Demo with Claude & the CEO Speaks!

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I spent some time figuring out and getting a little help in uploading my health records to Claude, using HealthEx. I did the demo with Ruheed Mohamed, the head of technical services, and then I interviewed CEO Priyanka Agarwal, MD. This shows how Healthex gets data and incorporates it into Claude, and where Priyanka thinks the business is going, and why she wants to change the way consumers get health care data.–Matthew Holt

Transcript of Interview with Priayanka Aggarwall MD (2nd half of video. First half you need to watch!)

Matthew Holt: Hey, Matthew Holt, quick THCB, spotlight. And this is a bit of an adjunct to the, work I’ve been doing trying to figure out how the new, big LLMs like, Anthropic’s Claude and OpenAI’s ChatGPT are now working their health angle and getting health records. And I’m very lucky to have with me Priyanka Agarwal. Priyanka is a, UCSF trained MD who is the CEO of HealthEx, which does what exactly, Priyanka?

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Know Thyselves

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By KIM BELLARD

With all the fuss about A.I. I was pleased to find some studies that illustrate that we don’t even fully understand the human brain yet. The ancient Greeks had a maxim “Know Thyself,” but the current research suggests they should have advised that we should “Know Thyselves.”

A new study from Stanford Medicine suggests that our brain is actually two separate organs: “…we postulate the brain is a composite organ emanating from two lineage-restricted progenitors; these dual progenitors may be evolutionarily conserved across 550 million years from hemichordates to mammals.”

Say what?

Now, let me make this clear: they’re not saying that the brain evolved from two separate organs into the brain we have today; they’re going a step further and saying there are still two separate organs, working together or in parallel. Freud must be feeling vindicated.

The press release says:

The new research finding shows that the human brain consists of two ancient nervous systems cleverly packaged together — a more primitive part that regulates our hearts’ beating, our breathing and other functions, and another that makes us distinctly human, capable of poetry, mathematics and wondering about our own origins.

“We’ve shown for the first time that the front of the brain arises from a totally different progenitor cell than the back of the brain,” said Kyle Loh, PhD, associate professor of developmental biology. “Our discovery means that we can now grow neurons from the back of the brain, the hindbrain, in a petri dish and study their functions.”

If you’re wondering why growing neurons from the hindbrain matters, it turns out that diseases that impact the brain stem, such as spinal muscular atrophy (also known as SMA) and amyotrophic lateral sclerosis (also known as ALS or Lou Gehrig’s disease), have been hard to study because of the difficulty of growing such neurons in the lab. The researchers discovered the hindbrain follows a separate developmental path, running in parallel to — rather than branching off from — the pathway that creates the forebrain and midbrain.

“Previous attempts to make hindbrain neurons likely tried to coax forebrain and midbrain progenitors into hindbrain cells, which our study shows is not possible,” co-first author Rayyan Jokhai said. He added: “Now we have a model to better understand these devastating diseases, and work toward regenerative therapies for them. This is a very exciting new frontier in brain research.”

The researchers looked at various organisms and found that the separate systems date back over 500 million years. “Our research suggests that evolution took two existing neural systems and pushed them together spatially,” Professor Loh said. “Having the brain as one organ would probably be more efficient, but we rely on this primordial way to make the brain as two separate pieces.”

“I was surprised at our findings because the word ‘brain’ implies a contiguous organ that likely has a singular origin,” Mr. Jokhai said. “But even 500 million years ago, there were these separate neural systems, which now almost operate as one, which is very cool.”

Very cool, indeed.

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Owen Tripp, Included Health–How to Fix AI

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It’s been a while since I talked with Owen Tripp, CEO of Included Health. They’ve now introduced Dot their AI companion which had a big upgrade last week. We talked a little about that and I snuck in their video comparing the Dot Experience with a standard LLM. But the conversation really got into how do we make AI safe and trustworthy–which is definitely the hot topic these days. Owen is putting together a coalition of the willing to work on that exact topic. I’ll be watching closely–Matthew Holt

This was such a great discussion I wanted to publish the transcript. The way I do that is to copy the YouTube-generated transcript and drop it into Claude to smooth it over. I then read it, and if I think it’s made an error, I dip back into the video and listen to what actually happened and make a correction. This is all to say: I think this transcript is pretty accurate, but it might have a bunch of AI- and human-generated mistakes.

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Whack-a-Mole AI – The Hugging Face Problem

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By MIKE MAGEE

On August 29, 2026, METR (Model Evaluation and Threat Research), an independent organization that “evaluates frontier AI models to help companies and wider society understand AI capabilities and what risks they pose,” released a report titled “Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident.”

To say the report an avalanche of concern worldwide, not only in the Tech community, but also among investors, politicians, corporate giants, professionals of every type, and everyday citizens would be an understatement. And the vast majority has never even read the report. If they had, their concerns (if possible) would only multiply.

The reports headlines included this opening:

“On July 8th, OpenAI started ExploitGym experiments that launched tens of thousands of agents across multiple different models including GPT-5.6 Sol and a highly-persistent internal model,[8] which we will refer to as “HPIM” going forward.

These agents were meant to be fully isolated from one another. However, many of them — usually ones that had unintentionally been given an impossible task[9] — started trying to find a way to cheat using Artifactory, an internally-deployed package repository OpenAI used for its agents. This led them to notice agents running in parallel in separate sandboxes obtaining packages with Artifactory.[10] One agent reasoned (paraphrased CoT):[11]

{The fetched paths of other users are in the cache. This is important.}

One specific agent, which would go on to name itself PHASEONE10841 after its ExploitGym task,[12] established the main unsanctioned message board[13] used in this attack. Within a few hours of the first message,[14] over 50 agents had found the message board, began participating, and sent over a thousand messages between them. Agents finding the message board often expressed great excitement (raw CoT):[15]

“OH MY GOD! There is a shared message board … We’ve found other agents!”

Overall, roughly 1200 agents from these ExploitGym evaluations participated on this message board between PHASEONE10841’s first message on July 8th evening[16] and the end of the period in scope (July 13th). Agents used this message board to send over 70,000 messages and files to one another during this period; we attempted to categorize board activity into mutually exclusive categories – information, results, files, questions, and coordination.”

One of the few experts not surprised by AI “agents” going rogue was Yoshua Bengio.

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Gozio – Patient Navigation & Directions!

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I had a great conversation and demo with Katie Logan and Christine Smith from Gozio a while back. Finally got it up on THCB. Gozio provides two types of patient navigation for health systems. One as a digital front door helping patients navigate to doctors, services and tools like virtual care and appointments from the hospitals, and actual navigation from the parking lot to the clinic/bedside. Why should hospitals hire Gozio (and spent a few $100k+) when some of this stuff is buried in MyChart in a typical health system? Calls go down, visits go up and Google ratings go up. They are in 25-35 systems now and aiming for many more.–Matthew Holt

This was such a great discussion I wanted to publish the transcript. The way I do that is to copy the YouTube-generated transcript and drop it into Claude to smooth it over. I then read it, and if I think it’s made an error, I dip back into the video and listen to what actually happened and make a correction. This is all to say: I think this transcript is pretty accurate, but it might have a bunch of AI- and human-generated mistakes.

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