Research into the design and development of AI models for rural healthcare isn’t hard to come by. However, that’s about as far as most of the investigations go.
The U.S. is one of 23 countries that consider workforce AI training and education only a medium priority. Indeed, our homeland has a less detailed plan than 13 other nations.
A primary aim of medical humanities as a field today is teaching medical students how to harmonize technological innovations with care models such that patients are treated as whole persons: They have not just bodies but also minds, relationships—and lives.
Dana Smetherman, MD, CEO of the American College of Radiology, says the organization wants to see transparent explainability of algorithms to increase public trust.
Federal lawmakers have a fresh resource to keep them up on healthcare AI that falls outside of formal FDA oversight. It comes in the form of a new report from the nonprofit Bipartisan Policy Center.
Patients who frequently use AI tend to readily trust AI-assisted diagnoses made by their physicians. Counterintuitively, however, those who would rank themselves among the very best-informed about AI tend to mistrust such diagnoses.
From the perspective of management science, healthcare is a complex adaptive system marked by intricate feedback loops and overlapping interdependencies. As such, the sector demands caution by those introducing large-language AI into its tangled webs.
