TY  - JOUR
AU  - Lekadir, Karim
AU  - Frangi, Alejandro F
AU  - Porras, Antonio R
AU  - Glocker, Ben
AU  - Cintas, Celia
AU  - Langlotz, Curtis P
AU  - Weicken, Eva
AU  - Asselbergs, Folkert W
AU  - Prior, Fred
AU  - Collins, Gary S
AU  - Kaissis, Georgios
AU  - Tsakou, Gianna
AU  - Buvat, Irène
AU  - Kalpathy-Cramer, Jayashree
AU  - Mongan, John
AU  - Schnabel, Julia A
AU  - Kushibar, Kaisar
AU  - Riklund, Katrine
AU  - Marias, Kostas
AU  - Amugongo, Lameck M
AU  - Fromont, Lauren A
AU  - Maier-Hein, Lena
AU  - Cerdá-Alberich, Leonor
AU  - Martí-Bonmatí, Luis
AU  - Cardoso, M Jorge
AU  - Bobowicz, Maciej
AU  - Shabani, Mahsa
AU  - Tsiknakis, Manolis
AU  - Zuluaga, Maria A
AU  - Fritzsche, Marie-Christine
AU  - Camacho, Marina
AU  - Linguraru, Marius George
AU  - Wenzel, Markus
AU  - De Bruijne, Marleen
AU  - Tolsgaard, Martin G
AU  - Goisauf, Melanie
AU  - Cano Abadía, Mónica
AU  - Papanikolaou, Nikolaos
AU  - Lazrak, Noussair
AU  - Pujol, Oriol
AU  - Osuala, Richard
AU  - Napel, Sandy
AU  - Colantonio, Sara
AU  - Joshi, Smriti
AU  - Klein, Stefan
AU  - Aussó, Susanna
AU  - Rogers, Wendy A
AU  - Salahuddin, Zohaib
AU  - Starmans, Martijn P A
TI  - FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.
JO  - The BMJ
VL  - 388
SN  - 0959-8154
CY  - London
PB  - BMJ Publ. Group
M1  - DKFZ-2025-00300
SP  - e081554
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:39909534
DO  - DOI:10.1136/bmj-2024-081554
UR  - https://inrepo02.dkfz.de/record/298583
ER  -