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DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence

Nature Medicine volume 27, pages 186–187 (2021)Cite this article

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As an increasing number of clinical decision-support systems driven by artificial intelligence progress from development to implementation, better guidance on the reporting of human factors and early-stage clinical evaluation is needed.

To the Editor—Recent years have seen an exponential growth in the number of artificial intelligence (AI) algorithms published in the medical literature, yet clinical impact in terms of patient outcomes remains to be demonstrated. One likely explanation for this so-called ‘AI chasm’ 1 is an overemphasis on the technical aspects of the proposed algorithms, with insufficient attention given to the factors that affect the interaction with their human users. As clinicians occupy, and are likely to keep occupying, the central role in patient care, it is essential to focus the development and evaluation of AI-based clinical algorithms on their potential to augment rather than replace human intelligence. However, AI-based decision-support systems pose unique challenges to the traditional medical decision-making process, such as their frequent lack of explainability (the so-called ‘black box’ problem) or their tendency to sometimes produce unexpected results. Hence, bridging algorithm development to bedside application while keeping humans at the center of the design and evaluation process is a complicated task, and current guidance is incomplete.

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Author information

Authors and Affiliations

  1. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK

    Baptiste Vasey & Peter McCulloch

  2. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK

    David A. Clifton

  3. Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Gary S. Collins

  4. NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK

    Gary S. Collins

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

    Alastair K. Denniston & Xiaoxuan Liu

  6. Eye Clinic, Cantonal Hospital Lucerne, Lucerne, Switzerland

    Livia Faes

  7. Moorfields Eye Hospital NHS Foundation Trust, London, UK

    Livia Faes & Xiaoxuan Liu

  8. Healthplus.ai B.V., Amsterdam, The Netherlands

    Bart F. Geerts

  9. Morgan Human Systems, Shrewsbury, UK

    Lauren Morgan

  10. Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

    Peter Watkinson

Consortia

The DECIDE-AI Steering Group

  • Baptiste Vasey
  • , David A. Clifton
  • , Gary S. Collins
  • , Alastair K. Denniston
  • , Livia Faes
  • , Bart F. Geerts
  • , Xiaoxuan Liu
  • , Lauren Morgan
  • , Peter Watkinson
  •  & Peter McCulloch

Corresponding author

Correspondence to Baptiste Vasey.

Ethics declarations

Competing interests

X.L. is an industry fellow (observer) with Hardian Health. D.A.C. declares academic grants from GlaxoSmithKline and personal fees from Oxford University Innovation, Biobeats and Sensyne Health, outside the context of this work. B.F.G. is CEO and Founder of Healthplus.ai and declares consulting fees from NLC Ventures Netherlands. L.M. is director and owner of Morgan Human Systems. P.W. reports grants from National Institute for Health Research, grants from Wellcome, grants from Sensyne Health, and personal fees from Sensyne Health, outside the submitted work.

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The DECIDE-AI Steering Group. DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat Med 27, 186–187 (2021). https://doi.org/10.1038/s41591-021-01229-5

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