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Nature Medicine (2025)
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Over the past few years, authoritative, trustworthy guidance for the clinical translation of artificial intelligence (AI) has formed mainly around two areas: responsible model development and validation; and prospective clinical trials. Initial work focused on building a good model, which generally means a model that demonstrates good performance, addresses an important clinical task, trains on the right data to address that task and could be used for some meaningful goal1. The model should then be assessed against sets of unseen cases to ensure it can generalize beyond the test set and can potentially be tested externally. Further practices are emerging around ongoing monitoring to detect model drift and performance changes. Collectively, these practices are characterized as responsible machine learning.
However, the distinctions between the in silico context and the clinical environment are substantial, which highlights the need for clinical evaluations. In 2020, the SPIRIT-AI and CONSORT-AI reporting guidelines were published to establish the minimum reporting criteria for the conduct of prospective, interventional clinical trials evaluating the impact of an AI model2,3. The DECIDE-AI guidelines were published shortly thereafter to address first-in-human feasibility trials of AI tools4. Regulatory frameworks emphasize the importance of clinical evidence, but precisely what kind and degree of evidence is needed for the approval of clinical AI applications is a matter of ongoing uncertainty5.
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We express our gratitude to the Women’s & Children’s Hospital AI Champions consumer group and the SickKids Medical AI Youth Council at The Hospital for Sick Children, whose members provided invaluable advice while developing this project plan. We particularly thank R. Fogel, L. Korkuti, R. Zhu and M. Ebrahimi, our consumer partners on this project whose advice and reflections have been invaluable as we advanced this project. We are grateful to the Aboriginal Health Unit staff members, whose partnership and collaboration have been invaluable in conducting this project in a way that addresses the particular needs and rights of Aboriginal and Torres Strait Islander peoples living in Australia. M.D.M. thanks the Hospital Research Foundation Group for salary support and the Centre for Augmented Reasoning at the Australian Institute for Machine Learning for research support for hosting the inaugural CANAIRI workshop. This project is partially funded by the Canadian Institute for Health Research.
Women’s and Children’s Health Network, Adelaide, South Australia, Australia
Melissa D. McCradden
Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
Melissa D. McCradden, Lauren Oakden-Rayner, Lyle J. Palmer, Carolyn Semmler & Lana Tikhomirov
SickKids Research Institute, Toronto, Ontario, Canada
Melissa D. McCradden, James A. Anderson, Anna Goldenberg, Abdullahi Mohamud & Devin Singh
Carnegie Mellon University, Pittsburgh, PA, USA
Alex John London
Department of Radiology, Emory University, Atlanta, GA, USA
Judy Wawira Gichoya
Duke Institute for Health Innovation, Durham, NC, USA
Mark Sendak
James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Lauren Erdman
University of Cincinnati School of Medicine, Cincinnati, OH, USA
Lauren Erdman
School of Public Policy and Administration at York University, Toronto, Ontario, Canada
Ian Stedman
Artificial Intelligence in Medicine Initiative, The Hospital for Sick Children, Toronto, Ontario, Canada
Ismail Akrout, Robert Greer, Abdullahi Mohamud & Devin Singh
Department of Bioethics, The Hospital for Sick Children, Toronto, Ontario, Canada
James A. Anderson
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
James A. Anderson
Tethyan Consulting, Canberra, Australian Capital Territory, Australia
Lesley-Anne Farmer
School of Computer Science, University of Toronto, Toronto, Ontario, Canada
Anna Goldenberg
Vector Institute for AI, Toronto, Ontario, Canada
Anna Goldenberg & Muhammad Mamdani
Canadian Institute for Advanced Research, Toronto, Ontario, Canada
Anna Goldenberg
Royal Australian and New Zealand College of Radiologists, Sydney, New South Wales, Australia
Yvonne Ho
Medical Devices and Product Quality Division, Health Products and Regulation Group, Australian Government Department of Health and Aged Care, Canberra, Australian Capital Territory, Australia
Yvonne Ho
Biomedical Informatics, Columbia University, New York, NY, USA
Shalmali Joshi
Columbia University Irving Medical Center, New York, NY, USA
Shalmali Joshi
Women’s and Children’s Hospital Research Centre, Adelaide, South Australia, Australia
Jennie Louise
Biostatistics Unit, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
Jennie Louise
Unity Health, Toronto, Ontario, Canada
Muhammad Mamdani
Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada
Muhammad Mamdani
Seattle Children’s Hospital, Seattle, WA, USA
Mjaye L. Mazwi
School of Public Health, University of Adelaide, Adelaide, South Australia, Australia
Lyle J. Palmer
Cropify, Adelaide, South Australia, Australia
Antonios Peperidis
Google Research, Mountain View, CA, USA
Stephen R. Pfohl
Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada
Mandy Rickard
School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
Carolyn Semmler & Lana Tikhomirov
Division of Biomedical Informatics, Department of Medicine, University of California San Diego, San Diego, CA, USA
Karandeep Singh
Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
Seyi Soremekun
Queensland Digital Health Centre, University of Queensland, Brisbane, Queensland, Australia
Anton H. van der Vegt
School of Computing Technologies, RMIT University, Melbourne, VIC, Australia
Karin Verspoor
Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
Xiaoxuan Liu
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
Xiaoxuan Liu
National Institute for Health and Care Research, Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
Xiaoxuan Liu
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Correspondence to Melissa D. McCradden.
S.R.P. is an employee of Google and may own stock as a part of a standard compensation package.
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McCradden, M.D., London, A.J., Gichoya, J.W. et al. CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare. Nat Med (2025). https://doi.org/10.1038/s41591-024-03364-1
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DOI: https://doi.org/10.1038/s41591-024-03364-1
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