MIT Sloan Management Review Article on Clinical AI Gets the Headlines, but Administrative AI May Be a Better Bet

AI for health care is all the rage. Who wouldn’t be excited about applications that could help detect cancer, diagnose COVID-19 or even dementia well before they are otherwise noticeable, or predict diabetes before its onset? Machine and deep learning have already been shown to make these outcomes possible.

Possible, that is, in the research lab. In health care, there is often a long lag between research findings and implementation at the bedside. In order for AI-driven advancements to become a clinical reality, they have to be submitted to and approved by the Food and Drug Administration (or similar regulatory authorities outside the United States) as “AI/machine learning-based software as a medical device.” Several hundred such applications have already been approved. But those tools then have to be accepted by clinicians, merged into their clinical workflows, integrated into electronic health records and other systems, and reimbursed by health insurers.

About the Author

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxford’s Saïd Business School, and a fellow of the MIT Initiative on the Digital Economy.

Randy Bean (@randybeannvp) is an industry thought leader, author, and CEO of NewVantage Partners, a strategic advisory and management consulting firm he founded in 2001. He is the author of the book Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

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