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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Artificial Intelligence in Medicine Lab (BCN-AIM), Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
Center for Computational Imaging & Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, United Kingdom; Medical Imaging Research Centre (MIRC), Cardiovascular Science and Electronic Engineering Departments, KU Leuven, Leuven, Belgium.
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, CO, Aurora, United States.
Department of Computing, Imperial College London, London, United Kingdom.
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2025 (English)In: BMJ. British Medical Journal, ISSN 0959-8146, E-ISSN 0959-535X, Vol. 388, article id e081554Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
BMJ Publishing Group Ltd, 2025. Vol. 388, article id e081554
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Artificial Intelligence
Identifiers
URN: urn:nbn:se:umu:diva-235707DOI: 10.1136/bmj-2024-081554ISI: 001427239400009Scopus ID: 2-s2.0-85217663528OAI: oai:DiVA.org:umu-235707DiVA, id: diva2:1939834
Funder
EU, Horizon 2020, 952172EU, Horizon 2020, 826494EU, Horizon 2020, 952179EU, Horizon 2020, 101034347EU, Horizon 2020, 101016775EU, Horizon 2020, 101100633EU, Horizon 2020, 101136670EU, Horizon 2020, 101057062EU, Horizon 2020, 101095435EU, Horizon 2020, 116074EU, Horizon Europe, 101057699EU, Horizon Europe, 101057849EU, Horizon Europe, 101080430EU, European Research Council, 757173EU, European Research Council, 884622EU, European Research Council, 101002198EU, European Research Council, 866504EU, European Research Council, 101044779NIH (National Institutes of Health)Independent Research Fund Denmark, 9131-00097BAvailable from: 2025-02-24 Created: 2025-02-24 Last updated: 2025-04-24Bibliographically approved

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Riklund, Katrine

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