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

Authors :
Lekadir, Karim
Feragen, Aasa
Fofanah, Abdul Joseph
Frangi, Alejandro F
Buyx, Alena
Emelie, Anais
Lara, Andrea
Porras, Antonio R
Chan, An-Wen
Navarro, Arcadi
Glocker, Ben
Botwe, Benard O
Khanal, Bishesh
Beger, Brigit
Wu, Carol C
Cintas, Celia
Langlotz, Curtis P
Rueckert, Daniel
Mzurikwao, Deogratias
Fotiadis, Dimitrios I
Zhussupov, Doszhan
Ferrante, Enzo
Meijering, Erik
Weicken, Eva
González, Fabio A
Asselbergs, Folkert W
Prior, Fred
Krestin, Gabriel P
Collins, Gary
Tegenaw, Geletaw S
Kaissis, Georgios
Misuraca, Gianluca
Tsakou, Gianna
Dwivedi, Girish
Kondylakis, Haridimos
Jayakody, Harsha
Woodruf, Henry C
Mayer, Horst Joachim
Aerts, Hugo JWL
Walsh, Ian
Chouvarda, Ioanna
Buvat, Irène
Tributsch, Isabell
Rekik, Islem
Duncan, James
Kalpathy-Cramer, Jayashree
Zahir, Jihad
Park, Jinah
Mongan, John
Gichoya, Judy W
Schnabel, Julia A
Kushibar, Kaisar
Riklund, Katrine
Mori, Kensaku
Marias, Kostas
Amugongo, Lameck M
Fromont, Lauren A
Maier-Hein, Lena
Alberich, Leonor Cerdá
Rittner, Leticia
Phiri, Lighton
Marrakchi-Kacem, Linda
Donoso-Bach, Lluís
Martí-Bonmatí, Luis
Cardoso, M Jorge
Bobowicz, Maciej
Shabani, Mahsa
Tsiknakis, Manolis
Zuluaga, Maria A
Bielikova, Maria
Fritzsche, Marie-Christine
Camacho, Marina
Linguraru, Marius George
Wenzel, Markus
De Bruijne, Marleen
Tolsgaard, Martin G
Ghassemi, Marzyeh
Ashrafuzzaman, Md
Goisauf, Melanie
Yaqub, Mohammad
Abadía, Mónica Cano
Mahmoud, Mukhtar M E
Elattar, Mustafa
Rieke, Nicola
Papanikolaou, Nikolaos
Lazrak, Noussair
Díaz, Oliver
Salvado, Olivier
Pujol, Oriol
Sall, Ousmane
Guevara, Pamela
Gordebeke, Peter
Lambin, Philippe
Brown, Pieta
Abolmaesumi, Purang
Dou, Qi
Lu, Qinghua
Osuala, Richard
Nakasi, Rose
Zhou, S Kevin
Napel, Sandy
Colantonio, Sara
Albarqouni, Shadi
Joshi, Smriti
Carter, Stacy
Klein, Stefan
Petersen, Steffen E
Aussó, Susanna
Awate, Suyash
Raviv, Tammy Riklin
Cook, Tessa
Mutsvangwa, Tinashe E M
Rogers, Wendy A
Niessen, Wiro J
Puig-Bosch, Xènia
Zeng, Yi
Mohammed, Yunusa G
Aquino, Yves Saint James
Salahuddin, Zohaib
Starmans, Martijn P A
Publication Year :
2023

Abstract

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2309.12325
Document Type :
Working Paper