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arxiv: 2309.12325 · v3 · pith:JZ5XRL34new · submitted 2023-08-11 · 💻 cs.CY · cs.AI· cs.CV· cs.LG

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

Karim Lekadir , Aasa Feragen , Abdul Joseph Fofanah , Alejandro F Frangi , Alena Buyx , Anais Emelie , Andrea Lara , Antonio R Porras
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An-Wen Chan Arcadi Navarro Ben Glocker Benard O Botwe Bishesh Khanal Brigit Beger Carol C Wu Celia Cintas Curtis P Langlotz Daniel Rueckert Deogratias Mzurikwao Dimitrios I Fotiadis Doszhan Zhussupov Enzo Ferrante Erik Meijering Eva Weicken Fabio A Gonz\'alez Folkert W Asselbergs Fred Prior Gabriel P Krestin Gary Collins Geletaw S Tegenaw Georgios Kaissis Gianluca Misuraca Gianna Tsakou Girish Dwivedi Haridimos Kondylakis Harsha Jayakody Henry C Woodruf Horst Joachim Mayer Hugo JWL Aerts Ian Walsh Ioanna Chouvarda Ir\`ene Buvat Isabell Tributsch Islem Rekik James Duncan Jayashree Kalpathy-Cramer Jihad Zahir Jinah Park John Mongan Judy W Gichoya Julia A Schnabel Kaisar Kushibar Katrine Riklund Kensaku Mori Kostas Marias Lameck M Amugongo Lauren A Fromont Lena Maier-Hein Leonor Cerd\'a Alberich Leticia Rittner Lighton Phiri Linda Marrakchi-Kacem Llu\'is Donoso-Bach Luis Mart\'i-Bonmat\'i M Jorge Cardoso Maciej Bobowicz Mahsa Shabani Manolis Tsiknakis Maria A Zuluaga Maria Bielikova Marie-Christine Fritzsche Marina Camacho Marius George Linguraru Markus Wenzel Marleen De Bruijne Martin G Tolsgaard Marzyeh Ghassemi Md Ashrafuzzaman Melanie Goisauf Mohammad Yaqub M\'onica Cano Abad\'ia Mukhtar M E Mahmoud Mustafa Elattar Nicola Rieke Nikolaos Papanikolaou Noussair Lazrak Oliver D\'iaz Olivier Salvado Oriol Pujol Ousmane Sall Pamela Guevara Peter Gordebeke Philippe Lambin Pieta Brown Purang Abolmaesumi Qi Dou Qinghua Lu Richard Osuala Rose Nakasi S Kevin Zhou Sandy Napel Sara Colantonio Shadi Albarqouni Smriti Joshi Stacy Carter Stefan Klein Steffen E Petersen Susanna Auss\'o Suyash Awate Tammy Riklin Raviv Tessa Cook Tinashe E M Mutsvangwa Wendy A Rogers Wiro J Niessen X\`enia Puig-Bosch Yi Zeng Yunusa G Mohammed Yves Saint James Aquino Zohaib Salahuddin Martijn P A Starmans
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classification 💻 cs.CY cs.AIcs.CVcs.LG
keywords future-aimedicalclinicalconsensushealthcaretrustworthydeploymentguideline
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Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

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