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

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

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