AI chatbots for doctors are hot. But there’s plenty of value ‘down the stack’ too, argues Corti CEO

From medical assistants to ‘AI infrastructure’ for healthcare

It’s unlikely one medical AI model will rule them all

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Jeremy Kahn is the AI editor at Fortune, spearheading the publication's coverage of artificial intelligence. He also co-

Among the most interesting AI stories this week was an item about a Boston-area startup called OpenEvidence that uses generativeAI to provide answers to clinical questions based data from leading medical journals. The free-to-use app has proved enormously popular among doctors, with some surveys suggesting at least 40% of all U.S. physicians are using OpenEvidence to stay on top of the latest medical research and to ensure they are offering the most up-to-date treatments to patients. On the back of that kind of viral growth, OpenEvidence was able to raise $210 million in a venture capital deal in July that valued the company at $3.5 billion. OpenEvidence is also the same company that a few weeks back said that its AI system was able to score 100% on the U.S. medical licensing exam. (See the “Eye on AI Numbers” section of the August 21st edition of this newsletter.) All of which may explain why, just a month later, the company is reportedly in talks for another venture deal that would almost double that valuation to $6 billion. (That’s according to a story in tech publication The Information which cited three unnamed people it said had knowledge of the discussions.)A lot of the use of OpenEvidence today would qualify as “shadow AI”—doctors are using it and finding value, but they aren’t necessarily admitting to their patients or employers that they are using it. They are also often using it outside enterprise-grade systems that are designed to provide higher-levels of security, data privacy, and compliance, and to integrate seamlessly with other business systems.Ultimately, that could be a problem, according to Andreas Cleve, the cofounder and CEO of Corti, a Danish medical AI company that is increasingly finding traction

AI infrastructure is a pivot for Corti, which was founded way back in 2013 and spent the first decade of its existence building its own speech recognition and language understanding systems for emergency services and hospitals. The company still markets its “Corti assistant” as a solution for healthcare systems that want an AI-power clinical scribe that can operate well in noisy hospital environments and integrate with electronic health records. But Cleve told me in a recent conversation that the company doesn’t see its future in selling a front-end solution to doctors, but instead selling key components in “the AI stack” to the companies that are offering front-end tools. 

“We tried to be both a product vendor for healthcare and an infrastructure vendor, and that meant competing with all the other apps in healthcare, and it was like, terrible,” he says.  Instead, Corti has decided its real value lies in providing the “healthcare grade” backend on which AI applications, many of them produced

Cleve also tells me that he thinks the giant, general purpose AI builders—the likes of OpenAI, Anthropic, and Google—are unlikely to conquer healthcare, despite the fact that they have been making moves to build models either fine-tuned or specifically-trained to answer clinical questions. He says this is because healthcare isn’t a single vertical, but rather a collection of highly-specialized niches, most of which are too narrow to be interesting to these tech behemoths. The note-taking needs of a GP in a relatively quiet office who needs to summarize a 10-minute consultation are quite different from a doctor working in the chaos and noise of a busy city ER, which are different again from a psychiatrist who needs to summarize not just a 10-minute consultation, but maybe an hour-long therapy session. As an example, Cleve says another Corti customer is a company in Germany that makes software just to help dentists automate billing based on audio transcripts of their sessions with patients. “They’re a vertical within a vertical,” he says. “But they are growing like 100% a year and have done so for several years. But they are super niche.”

It will be interesting to watch Corti going forward. Perhaps Cleve is correct that the AI stack is  wide enough, deep enough and varied enough to create opportunities for lots of different vertical and regional players. Or, it could be that OpenAI, Microsoft, and Google devour everyone else. Time will tell.

With that, here’s more AI news.

Jeremy Kahnjeremy.kahn.com

British lawmakers accuse Google DeepMind of ‘breach of trust’ over delayed Gemini 2.5 Pro safety report—

Microsoft unveils first frontier-level language models built in-house. The company said it has begun publicly testing MAI-1-preview, its first large foundation AI model built fully in-house, as well as MAI-Voice, a fast voice generation model that is small enough to run on a single GPU. The new models mark a significant step

OpenAI says it will add parental controls and additional safeguards to ChatGPT. The company said it would, within the next month, allow parents to link their accounts to those of their teenage children, giving them more control over the kids' interaction with ChatGPT. It also said that it would soon institute better safeguards in general, actively screening interactions for signs of emotional distress on the part of users and routing these conversations to its GPT-5 Thinking model, which the company says does a better job of adhering to guardrails meant to prevent the model from encouraging self-harm or delusional behavior. The moves come after a high-profile lawsuit accused the company's ChatGPT model of encouraging the suicide of a 16-year old teen and several other cases in which people allege ChatGPT encouraged suicide, self-harm, or violence. You can read more here from Axios.

Anthropic valued at $183 billion after $13 billion venture capital round. The AI company announced that it had raised $13 billion Series F funding round led

More evidence emerges that AI may be leading to job losses. Last week in Eye on AI, I covered research from economists at Stanford University whose research indicated that AI was leading to job losses, particularly among entry level employees, in professions that were highly-exposed to AI. This week, more evidence emerges from another well-designed study, this one carried out

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Preventing 'parasocial' chatbots.’ It’s increasingly clear that chatbots can encourage ‘parasocial’ relationships, where a user develops a harmful emotional attachment to the chatbot or the chatbot encourages the user to engage in self-harm of some kind. The parents of several teenagers who took their own lives after conversations with chatbots are now suing AI companies, saying they did not do enough to prevent chatbots from encouraging self-harm. And, short of suicides, there is other mounting evidence of people developing harmful chatbot dependencies.Well, a new benchmark from researchers at Hugging Face, called INTIMA (Interactions and Machine Attachment Benchmark), aims to evaluate LLMs on their “companionship-seeking” behavior. The benchmark looks at 31 distinct behaviors across four different categories of interaction and 368 prompts. Testing Gemma-3, Phi-4, o3-mini, and Claude-4, researchers found that models more often reinforced companionship than maintained boundaries, though they varied: for instance, Claude was more likely to resist personification, while Gemma reinforced intimacy. You can read the Hugging Face paper here.At the same time, researchers from Aligned AI, an Oxford startup I’ve covered before, published research showing that one LLM can be used to successfully screen the outputs of another LLM for parasocial behavior and then prompt that chatbot to steer the conversation in a less harmful direction. Aligned AI wanted to show that major AI model producers could implement such systems simply, if they wished (but that they were too often choosing to “optimize for user engagement” instead.) You can read more from Aligned AI’s blog here.