Eric Topol, Founder and Director of Scripps Research Translational Institute at The Scripps Research Institute, shared on X:
“The 3 reasons why AI won’t reduce clinician jobs:
- Jevon’s paradox (more efficiency, more use)
- ‘Lump of labor’ fallacy (the type of work changes)
- Tasks ≠ Skills.”
Ryan C. Augustin, Medical Oncologist at Mayo Clinic, shared Eric Topol’s post, adding:
“I think the biggest questions for practice/operations in the next two years:
- How specifically will practice become not only more efficient, but increase quantity and (hopefully) quality of care? Is that seeing more patients? Lessening burn out? Eventually, institutions will need to spend a decent amount of money on these systems…what is the cost/benefit analysis?
- What type of work will necessarily need to decrease, and what kind of clinician work will be most valued/emphasized? We’ve been saying ‘less manual data gathering, more time with patients’ for years, even before AI. As patient cases become ever more complex (esp in oncology), will this golden dream ever become reality? We need to make sure the ‘capable human in the loop’ isn’t stuck on validating/reviewing AI output and can actually get more time with the patient. Next gen summary tools need to rate outputs based on % confidence so clinicians can easily decide which of the dozens of data points actually require human review.”
Title: Artificial Intelligence and the Future of the Clinical Workforce
Author: Dhruv Khullar
Other articles featuring Eric Topol on OncoDaily.