Might ChatGPT Transform Healthcare? Pioneering Leaders Are Working Forward to Find Out

Perhaps no other phenomenon in U.S. healthcare has been the subject of a greater amount of hype, expectation, and confusion, all mixed up together, than has the emergence of artificial intelligence (AI). When it comes to the famous “Gartner hype cycle,” the development of AI for clinical and clinical-operational uses in patient care organizations might at this moment be anywhere between the “Peak of Inflated Expectations,” the “Trough of Disillusionment,” or the “Slope of Enlightenment,” depending on one’s perceptions.

Further complicating perceptions has been the emergence in the past several months of ChatGPT, a large language model developed by OpenAI launched late last year. That launch has intensified the complexity of a scenario in which needs, expectations, early advances, and yes, of course, many stalls and outright failures, are adding up to the current landscape, one that couldn’t possibly be more layered and complicated.

Looking at the overall landscape nationwide, Mathaeus Dejori and Ryan Nellis see a natural evolution—with bumps along the way—ahead for patient care organization leaders. Dejori is chief data scientist and AI lead, and Nellis is vice president and general manager, at PINC AI^TM Stanson Health, a subsidiary of the Charlotte-based Premier Inc. health alliance. PINC AI^TM Stanson Health provides a solution that provides point-of-care clinical decision support.

There’s a lot of work ahead, naturally. “These models are very powerful, but they’re very biased,” Dejori says. “And when you start to measure how good they are—these models are trained on internet data, and aren’t necessarily aligned to your domain. So you have to make sure each large language model is tamed to your domain and works in your domain. You can use ChatGPT, but in terms of guaranteeing accuracy and safety, everyone is struggling now to figure that out. And these models are powerful, and the cost aspect is interesting, because you have powerful models with billions of parameters. But do you really need such powerful, expensive models? We do real-time decision support. And you cannot get a real-time response of hundreds of billions of parameters model. So you need for clinicians to play with these models.”

Read full article here.