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@ARTICLE{Lekadir:298583,
      author       = {K. Lekadir and A. F. Frangi and A. R. Porras and B. Glocker
                      and C. Cintas and C. P. Langlotz and E. Weicken and F. W.
                      Asselbergs and F. Prior and G. S. Collins and G. Kaissis and
                      G. Tsakou and I. Buvat and J. Kalpathy-Cramer and J. Mongan
                      and J. A. Schnabel and K. Kushibar and K. Riklund and K.
                      Marias and L. M. Amugongo and L. A. Fromont and L.
                      Maier-Hein$^*$ and L. Cerdá-Alberich and L. Martí-Bonmatí
                      and M. J. Cardoso and M. Bobowicz and M. Shabani and M.
                      Tsiknakis and M. A. Zuluaga and M.-C. Fritzsche and M.
                      Camacho and M. G. Linguraru and M. Wenzel and M. De Bruijne
                      and M. G. Tolsgaard and M. Goisauf and M. Cano Abadía and
                      N. Papanikolaou and N. Lazrak and O. Pujol and R. Osuala and
                      S. Napel and S. Colantonio and S. Joshi and S. Klein and S.
                      Aussó and W. A. Rogers and Z. Salahuddin and M. P. A.
                      Starmans},
      collaboration = {F.-A. Consortium},
      othercontributors = {A. Feragen and A. J. Fofanah and A. Buyx and A. Emelie and
                          A. Lara and A.-W. Chan and A. Navarro and B. O. Botwe and B.
                          Khanal and B. Beger and C. C. Wu and D. Rueckert and D.
                          Mzurikwao and D. I. Fotiadis and D. Zhussupov and E.
                          Ferrante and E. Meijering and F. A. González and G. P.
                          Krestin and G. S. Tegenaw and G. Misuraca and G. Dwivedi and
                          H. Kondylakis and H. Jayakody and H. C. Woodruf and H. J.
                          Mayer and H. J. Aerts and I. Walsh and I. Chouvarda and I.
                          Tributsch and I. Rekik and J. Duncan and J. Zahir and J.
                          Park and J. W. Gichoya and K. Mori and L. Rittner and L.
                          Phiri and L. Marrakchi-Kacem and L. Donoso-Bach and M.
                          Bielikova and M. Ghassemi and M. Ashrafuzzaman and M. Yaqub
                          and M. M. E. Mahmoud and M. Elattar and N. Rieke and O.
                          Díaz and O. Salvado and O. Sall and P. Guevara and P.
                          Gordebeke and P. Lambin and P. Brown and P. Abolmaesumi and
                          Q. Dou and Q. Lu and R. Nakasi and S. K. Zhou and S.
                          Albarqouni and S. Carter and S. E. Petersen and S. Awate and
                          T. Riklin Raviv and T. Cook and T. E. M. Mutsvangwa and W.
                          J. Niessen and X. Puig-Bosch and Y. Zeng and Y. G. Mohammed
                          and Y. S. J. Aquino},
      title        = {{FUTURE}-{AI}: international consensus guideline for
                      trustworthy and deployable artificial intelligence in
                      healthcare.},
      journal      = {The BMJ},
      volume       = {388},
      issn         = {0959-8154},
      address      = {London},
      publisher    = {BMJ Publ. Group},
      reportid     = {DKFZ-2025-00300},
      pages        = {e081554},
      year         = {2025},
      abstract     = {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.},
      cin          = {E130},
      ddc          = {610},
      cid          = {I:(DE-He78)E130-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39909534},
      doi          = {10.1136/bmj-2024-081554},
      url          = {https://inrepo02.dkfz.de/record/298583},
}