Peer-Reviewed Publication
Cell2026;189(19):5893-5904.September 17, 2026Journal Article
Avoiding common failures in AI for health and medicine.
Olawale Salaudeen1, Haoran Zhang2, Eileen Kim3, Karandeep Singh4, Marzyeh Ghassemi5
1Department of Electrical Engineering and Computer Science, Massachussets Institute of Technology, Cambridge, MA, USA. Electronic address: olawale@mit.edu.
2Department of Electrical Engineering and Computer Science, Massachussets Institute of Technology, Cambridge, MA, USA.
3Division of General Internal Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA; Joan & Irwin Jacobs Center for Health Innovation, University of California San Diego Health, La Jolla, CA, USA.
4Joan & Irwin Jacobs Center for Health Innovation, University of California San Diego Health, La Jolla, CA, USA; Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
5Department of Electrical Engineering and Computer Science, Massachussets Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Abstract
The widespread adoption of artificial intelligence (AI) in healthcare necessitates reliable AI systems. Reliability refers to clinically acceptable performance across contexts such as patient and clinician populations and over time under real-world deployment conditions. This review synthesizes common reliability failure modes in predictive and generative AI systems, which include erroneous model…
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