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CANAIRI: the Collaboration for Translational Artificial Intelligence Trials in healthcare

Nature Medicine volume 31, pages 9–11 (2025)Cite this article

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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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Fig. 1: Comparing standard care, silent trials and translational trials.

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Acknowledgements

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.

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Authors and Affiliations

  1. Women’s and Children’s Health Network, Adelaide, South Australia, Australia

    Melissa D. McCradden

  2. 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

  3. SickKids Research Institute, Toronto, Ontario, Canada

    Melissa D. McCradden, James A. Anderson, Anna Goldenberg, Abdullahi Mohamud & Devin Singh

  4. Carnegie Mellon University, Pittsburgh, PA, USA

    Alex John London

  5. Department of Radiology, Emory University, Atlanta, GA, USA

    Judy Wawira Gichoya

  6. Duke Institute for Health Innovation, Durham, NC, USA

    Mark Sendak

  7. James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Lauren Erdman

  8. University of Cincinnati School of Medicine, Cincinnati, OH, USA

    Lauren Erdman

  9. School of Public Policy and Administration at York University, Toronto, Ontario, Canada

    Ian Stedman

  10. Artificial Intelligence in Medicine Initiative, The Hospital for Sick Children, Toronto, Ontario, Canada

    Ismail Akrout, Robert Greer, Abdullahi Mohamud & Devin Singh

  11. Department of Bioethics, The Hospital for Sick Children, Toronto, Ontario, Canada

    James A. Anderson

  12. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada

    James A. Anderson

  13. Tethyan Consulting, Canberra, Australian Capital Territory, Australia

    Lesley-Anne Farmer

  14. School of Computer Science, University of Toronto, Toronto, Ontario, Canada

    Anna Goldenberg

  15. Vector Institute for AI, Toronto, Ontario, Canada

    Anna Goldenberg & Muhammad Mamdani

  16. Canadian Institute for Advanced Research, Toronto, Ontario, Canada

    Anna Goldenberg

  17. Royal Australian and New Zealand College of Radiologists, Sydney, New South Wales, Australia

    Yvonne Ho

  18. 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

  19. Biomedical Informatics, Columbia University, New York, NY, USA

    Shalmali Joshi

  20. Columbia University Irving Medical Center, New York, NY, USA

    Shalmali Joshi

  21. Women’s and Children’s Hospital Research Centre, Adelaide, South Australia, Australia

    Jennie Louise

  22. Biostatistics Unit, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia

    Jennie Louise

  23. Unity Health, Toronto, Ontario, Canada

    Muhammad Mamdani

  24. Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada

    Muhammad Mamdani

  25. Seattle Children’s Hospital, Seattle, WA, USA

    Mjaye L. Mazwi

  26. School of Public Health, University of Adelaide, Adelaide, South Australia, Australia

    Lyle J. Palmer

  27. Cropify, Adelaide, South Australia, Australia

    Antonios Peperidis

  28. Google Research, Mountain View, CA, USA

    Stephen R. Pfohl

  29. Division of Urology, Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada

    Mandy Rickard

  30. School of Psychology, University of Adelaide, Adelaide, South Australia, Australia

    Carolyn Semmler & Lana Tikhomirov

  31. Division of Biomedical Informatics, Department of Medicine, University of California San Diego, San Diego, CA, USA

    Karandeep Singh

  32. Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK

    Seyi Soremekun

  33. Queensland Digital Health Centre, University of Queensland, Brisbane, Queensland, Australia

    Anton H. van der Vegt

  34. School of Computing Technologies, RMIT University, Melbourne, VIC, Australia

    Karin Verspoor

  35. Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK

    Xiaoxuan Liu

  36. University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

    Xiaoxuan Liu

  37. National Institute for Health and Care Research, Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK

    Xiaoxuan Liu

Authors
  1. Melissa D. McCradden
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  30. Xiaoxuan Liu
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Corresponding author

Correspondence to Melissa D. McCradden.

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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 31, 9–11 (2025). https://doi.org/10.1038/s41591-024-03364-1

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