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Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group

Nature Medicine volume 26, pages 807–808 (2020)Cite this article

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To the Editor — Artificial intelligence (AI)-based technologies dominate medical headlines and are routinely touted as the panacea for a number of longstanding deficiencies across health systems globally. Stakeholders from healthcare, government, computer science and industry backgrounds are confident that AI can be positioned to tackle (1) the high rate of avoidable medical errors, (2) workflow inefficiencies and (3) delivery inefficiencies associated with contemporary healthcare provision1. Despite these lofty ambitions, the integration of AI into everyday practice within the health sector has been limited thus far.

So far, the majority of AI interventions that are close to translation are predominantly in the field of medical diagnostics. In the current paradigm, diagnostic investigations require timely interpretation from an expert clinician in order to generate a diagnosis and to direct subsequent episodes of care. The recurring issue with this system is that diagnostic services are inundated with large volumes of work, which can often supersede workforce capacity. In order to address this, diagnostic AI algorithms, with notable examples targeted toward breast cancer, lung cancer and diabetic retinopathy, have positioned themselves as medical devices2 that may achieve diagnostic accuracy comparable to that of an expert clinician while concurrently alleviating health-resource use. However, although this paradigm shift may seem imminent, it is crucial to note that much of the evidence supporting diagnostic algorithms has been disseminated in the absence of AI-specific reporting guidelines. Without this guidance, and in a relatively nascent areas, key stakeholders are poorly placed to appraise quality and compare diagnostic accuracy between studies.

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Acknowledgements

Infrastructure support was provided by the NIHR Imperial Biomedical Research Centre.

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Authors and Affiliations

  1. Institute of Global Health Innovation, Imperial College London, London, UK

    Viknesh Sounderajah, Hutan Ashrafian, Ravi Aggarwal, Alan Karthikesalingam, Dominic King & Ara Darzi

  2. Department of Surgery and Cancer, Imperial College London, London, UK

    Viknesh Sounderajah, Hutan Ashrafian, Ravi Aggarwal, Alan Karthikesalingam, Dominic King, Sheraz R. Markar & Ara Darzi

  3. DeepMind, London, UK

    Jeffrey De Fauw

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

    Alastair K. Denniston & Xiaoxuan Liu

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

    Alastair K. Denniston & Xiaoxuan Liu

  6. Health Data Research UK, London, UK

    Alastair K. Denniston & Xiaoxuan Liu

  7. Public Health England, London, UK

    Felix Greaves

  8. Department of Radiology, University of Ottawa, Ottawa, Canada

    Matthew D. F. McInnes

  9. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada

    Matthew D. F. McInnes

  10. Division of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Trishan Panch

  11. School of Public Health, Imperial College London, London, UK

    Jonathan Pearson-Stuttard

  12. Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore

    Daniel S. W. Ting

  13. Journal of the American Medical Association, Chicago, IL, USA

    Robert M. Golub

  14. Ottawa Hospital Research Institute, Ottawa, Canada

    David Moher

  15. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, the Netherlands

    Patrick M. Bossuyt

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  1. Viknesh Sounderajah
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  2. Hutan Ashrafian
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  16. David Moher
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  17. Patrick M. Bossuyt
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  18. Ara Darzi
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Corresponding author

Correspondence to Hutan Ashrafian.

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Competing interests

D.K., J.D.F. and A.K. are employees of Google Health. A.D. is an adviser at Google DeepMind/Health.

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Sounderajah, V., Ashrafian, H., Aggarwal, R. et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med 26, 807–808 (2020). https://doi.org/10.1038/s41591-020-0941-1

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