Nabla, a digital health startup, launches Copilot, using GPT-3 to turn patient conversations into action

Healthcare has been pegged as a prime candidate for more AI applications -- both to aid in clinical work and to lighten some of the more time-consuming administrative burdens that come around clinical care. Now, Nabla, the digital health startup out of Paris co-founded by AI entrepreneur Alexandre Lebrun, claims to be the first to build a tool using GPT-3 to help physicians do their work -- more specifically, their paperwork.

Copilot, as Nabla's new service is called, is launching today as a digital assistant for doctors accessed initially as a Chrome extension to help transcribe and repurpose information from video conversations, with plans for an in-person consultation tool to launch in a few weeks.

Following along as doctors see patients, Copilot automatically translates those conversations into different document-based endpoints -- eg, prescriptions, follow up appointment letters, consultation summaries -- that typically result from those meetings. It's based around GPT-3, the language model built by OpenAI that is used to generate human text, which is powering hundreds of applications, including ChatGPT from OpenAI itself.

Nabla was one of the first companies to experiment with GPT-3 when it was released in 2020. While Nabla is currently using GPT-3 (as a paying customer) as the basis of Copilot, Lebrun tells me that the longer term goal, approaching fast, is to build its own large language model customized to the particular language and needs in medicine and healthcare, to power Copilot, whatever else Nabla builds in future, and potentially applications for others, too.

The early version already has some traction, the startup says: it's in use by practitioners in the U.S. and France, as well as around 20 digital and in-person clinics "with significant medical teams."

The jury is still out on what large-scale, long-term uses we'll see for generative AI technologies -- and whether they and the large language models that power them will provide net benefits or net losses to our world; and whether they will make any money in the process.

In the meantime, healthcare has been one of the big industries that people have been watching with interest to see how it responds to these developments, roughly down two corridors of development. First, where it could be used for clinical assistance, for example as described in this piece co-authored by Harvard Medical School doctors and academics on using ChatGPT to diagnose patients; and second, in automating for more repetitive functions, as illustrated in this Lancet piece on the future of discharge summaries.

A lot of that work is still very much in its early stages, not least because healthcare is particularly sensitive.

"With all large language models, there is a risk," Lebrun said in an interview. "It's incredibly powerful, but five percent of the time it will be completely wrong and you have no way to control that. But in healthcare we [literally] can't live with a 5% error rate."

Yet in many regards, healthcare seems like a prime area to be infused with AI: clinicians are oversubscribed with patients and burned out; globally we are facing a chronic shortage of doctors partly as a result of so many leaving the profession, and partly because of the work demanded of them. On top of seeing patients, they have to dedicate time to being administrators, with a lot of very specific and formal pieces of documentation to get through to record appointment data and plan what comes next demanded both by rules and regulations, but also patients themselves. Alongside all this, there are sometimes unfortunately instances of human error.

On the other side, though, a number of steps in medical care have already been digitized, paving the way for patients and clinicians being more open to using more digital tools to help with the rest.

That thinking was in part what motivated Alexandre LeBrun to start Nabla in the first place, and to target Copilot specifically first at helping physicians with administrative tasks -- not examining or counseling patients, or other clinical work.

LeBrun has a history in building language-based applications. In 2013, he sold his startup VirtuOz, described back then as the "Siri for enterprise", to Nuance to spearhead its development of digital assistant tech for businesses. He then founded and eventually sold his next startup, Wit.ai, to Facebook, where he and his team then worked on the social network's foray into chatbots in Messenger. He then put in time at FAIR, Facebook's AI research centre in Paris.

Those early tools for enterprises to interact with customers were largely pitched as marketing and customer loyalty aids, but Lebrun believed they could be applied in less fuzzy scenarios, too.