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Lekadir, K.* ; Frangi, A.F.* ; Porras, A.R.* ; Glocker, B.* ; Cintas, C.* ; Langlotz, C.P.* ; Weicken, E.* ; Asselbergs, F.W.* ; Prior, F.* ; Collins, G.S.* ; Kaissis, G.* ; Tsakou, G.* ; Buvat, I.* ; Kalpathy-Cramer, J.* ; Mongan, J.* ; Schnabel, J.A. ; Kushibar, K.* ; Riklund, K.* ; Marias, K.* ; Amugongo, L.M.* ; Fromont, L.A.* ; Maier-Hein, L.* ; Cerdá-Alberich, L.* ; Martí-Bonmatí, L.* ; Cardoso, M.J.* ; Bobowicz, M.* ; Shabani, M.* ; Tsiknakis, M.* ; Zuluaga, M.A.* ; Fritzsche, M.C.* ; Camacho, M.* ; Linguraru, M.G.* ; Wenzel, M.* ; De Bruijne, M.* ; Tolsgaard, M.G.* ; Goisauf, M.* ; Cano Abadía, M.* ; Papanikolaou, N.* ; Lazrak, N.* ; Pujol, O.* ; Osuala, R.* ; Napel, S.* ; Colantonio, S.* ; Joshi, S.* ; Klein, S.* ; Aussó, S.* ; Rogers, W.A.* ; Salahuddin, Z.* ; Starmans, M.P.A.*

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.

BMJ 388:e081554 (2025)

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	Open Access Gold (Paid Option)

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Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles-fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Cost-effectiveness; Performance; Diagnosis; Trials; Bias; Technology; Medicine; Disease

ISSN (print) / ISBN 0959-535X

e-ISSN 1756-1833

Journal BMJ

Quellenangaben Volume: 388, Article Number: e081554

Publisher British Medical Association

Publishing Place London

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute for Machine Learning in Biomed Imaging (IML)

Grants European Research Council
Royal Academy of Engineering, Hospital Clinic Barcelona
Carnegie Cooperation New York, Human frontier science programme
Natural Sciences and Engineering Research Council of Canada (NSERC)
Australian National Health and Medical Research Council
United States Department of Defence
Korean government
A*STAR Career Development Award
Agency for Science, Technology and Research (A*STAR)
European Union's Horizon Europe
European Union
National Institute for Health and Care Research Barts Biomedical Research Centre
Infraestructura de Medicina de Precisin asociada a la Ciencia y la Tecnologia - Instituto de Salud Carlos III
Fondo Europeo de Desarrollo Regional (FEDER)
Cancer Research UK programme
National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health
AIMED
Ministry of Science, Innovation and Universities of the Government of Spain
European Research Council (ERC)
Horizon Europe - Pillar II
Wellcome Flagship Programme
Google Research, Independent Research Fund Denmark
Netherlands Organisation for Scientific Research (NWO)
Ministry of Science, Technology and Innovation of Colombia
Gordon and Betty Moore Foundation
Google Award for Inclusion Research, Fraunhofer Heinrich Hertz Institute, US National Institutes of Health, National Council for Scientific and Technological Development (CNPq)
European Heart Network
NIBIB/University of Chicago (MIDRC) , Hong Kong Research Grants Council Theme-based Research Scheme
Ministry of High Education of Tunisia
Fogarty International Center of the National Institutes of Health
Natural Science Foundation of China
Israel Science Foundation
National Institutes of Health (NIH)
Dutch Cancer Society (KWF Kankerbestrijding)
U.S. Department of Defense (DOD)