Foundation models for generalist medical artificial intelligence

• Published in: Nature (London) Vol. 616; no. 7956; pp. 259 - 265
• Main Authors: Moor, Michael, Banerjee, Oishi, Abad, Zahra Shakeri Hossein, Krumholz, Harlan M., Leskovec, Jure, Topol, Eric J., Rajpurkar, Pranav
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.04.2023; Nature Publishing Group
• Subjects: 631/114; 692/700; Artificial Intelligence; Datasets; Datasets as Topic; Diagnostic Imaging; Electronic Health Records; Electronic medical records; Genomics; Humanities and Social Sciences; Humans; Knowledge; Laboratories; Marginalized groups; Medicine; Modelling; multidisciplinary; Perspective; Pneumonia; Queries; Science; Science (multidisciplinary); Unsupervised Machine Learning

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a div...