Evidence map›Paper›PMID 37045921›Full record

ReviewNature2023

Foundation models for generalist medical artificial intelligence.

Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, Pranav Rajpurkar

Registry-linked trialOpen access · hybridAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07500428 (Construction of a Standardized Benchmark Evaluation System for Intelligent Breast Ultrasound Image Interpretation and Systematic Performance Assessment of Multimodal Artificial Intelligence Models Based on ACR BI-RADS v2025 Criteria), which is not on this map. Cited by 814 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
814citing papers in PubMed, 3 pooled it
243.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07500428 recruitingnot on this mapstarted 2026, after this paper: background citation

Construction of a Standardized Benchmark Evaluation System for Intelligent Breast Ultrasound Image Interpretation and Systematic Performance Assessment of Multimodal Artificial Intelligence Models Based on ACR BI-RADS v2025 Criteria

TypeobservationalSponsorPeking Union Medical College HospitalRan2026 to 2027Enrolled1,380ConditionsBreast Neoplasms, Breast Diseases, UltrasonographyArmsMultimodal AI Model Diagnostic Evaluation
3 · Its place in the literature

Who cites it

814 citing papers in PubMed, 3 syntheses or guidelines pooled it, 1,405 citations in OpenAlex.

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754 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Michael Moor *Department of Computer Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-3401-2937
Oishi Banerjee *Department of Biomedical Informatics, Harvard University, Cambridge, MA, USA.
Zahra Shakeri Hossein AbadInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Harlan M KrumholzYale University School of Medicine, Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-2046-127X
Jure LeskovecDepartment of Computer Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-5411-923X
Eric J TopolScripps Research Translational Institute, La Jolla, CA, USA. etopol@scripps.edu.ORCID http://orcid.org/0000-0002-1478-4729
Pranav RajpurkarDepartment of Biomedical Informatics, Harvard University, Cambridge, MA, USA. pranav_rajpurkar@hms.harvard.edu.ORCID http://orcid.org/0000-0002-8030-3727
Harvard University · USStanford University · USScripps Research Institute · USUniversity of Toronto · CAYale New Haven Hospital · US

Funding

Scripps Translational Science InstituteUL1TR001114 · NCATS · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2013 to 2017
$24.0M
Stanford Tissue Mapping CenterU54HG010426 · NHGRI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2018 to 2021
$5.1M
NCATS NIH HHS UL1TR001114NHGRI NIH HHS 3U54HG010426-04S1NINDS NIH HHS R61 NS11865
6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceMedicineDatasets as TopicDiagnostic ImagingElectronic Health RecordsGenomicsHumansUnsupervised Machine Learning

Identifiers

PMID37045921
OpenAlexW4365143687

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.