BOSTON — Scientific journals have become something of a Mad Libs game for GPT: Artificial intelligence can now detect _____, or speedily tell the difference between _____ and _____. But which of these studies are actually important? How can clinicians sort them out from one another?
At a recent AI conference, Atman Health chief medical officer and Brigham and Women’s associate physician Rahul Deo boiled the issue down in a single slide: the riskiest, most impactful studies draw far less attention these days than the rest of the research.
The highest-impact AI models would be those that figure out how to replace the most complex physician tasks with automation. Those are followed by studies that take steps toward that goal, including models that predict patient risk, clinical decision support models, and language models that automate rote office tasks. And then, at the bottom, is the “everything else” category: studies that might seem impressive, but don’t actually move the needle.
About the Author Reprints
To submit a correction request, please visit our Contact Us page.
STAT encourages you to share your voice. We welcome your commentary, criticism, and expertise on our subscriber-only platform, STAT+ Connect