The transformation of patient-clinician relationships with AI-based medical advice

Oded Nov, Yindalon Aphinyanaphongs, Yvonne W. Lui, Devin Mann, Maurizio Porfiri, Mark Riedl, John Ross Rizzo, Batia Wiesenfeld

Research output: Contribution to journalReview articlepeer-review

Abstract

The transformation of patient-clinician relationships with AI-based medical advice is discussed. many new tools are based on entirely new ‘black-box’ AI-based technologies, whose inner workings are likely not fully understood by patients or clinicians. Most patients with Type 1 diabetes now use continuous glucose monitors and insulin pumps to tightly manage their disease. Their clinicians carefully review the data streams from both devices to recommend dosage adjustments. Recently new automated recommender systems to monitor and analyze food intake, insulin doses, physical activity, and other factors influencing glucose levels, and provide data-intensive, AI-based recommendations on how to titrate the regimen, are in different stages of FDA approval using ‘black box’ technology, which is an alluring proposition for a clinical scenario that requires identification of meaningful patterns in complex and voluminous data.

Original languageEnglish (US)
Pages (from-to)46-48
Number of pages3
JournalCommunications of the ACM
Volume64
Issue number3
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • General Computer Science

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