Foundation models for generalist medical artificial intelligence

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...

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Veröffentlicht in:Nature (London) Jg. 616; H. 7956; S. 259 - 265
Hauptverfasser: Moor, Michael, Banerjee, Oishi, Abad, Zahra Shakeri Hossein, Krumholz, Harlan M., Leskovec, Jure, Topol, Eric J., Rajpurkar, Pranav
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 13.04.2023
Nature Publishing Group
Schlagworte:
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
ISSN:0028-0836, 1476-4687, 1476-4687
Online-Zugang:Volltext
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Zusammenfassung: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 diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets. This review discusses generalist medical artificial intelligence, identifying potential applications and setting out specific technical capabilities and training datasets necessary to enable them, as well as highlighting challenges to its implementation.
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ISSN:0028-0836
1476-4687
1476-4687
DOI:10.1038/s41586-023-05881-4