Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI.

Journal: Nature medicine
PMID: 35585198

Abstract

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Authors

  • Baptiste Vasey
    Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
  • Myura Nagendran
    Division of Anaesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Imperial College London, UK myura.nagendran@imperial.ac.uk.
  • Bruce Campbell
    Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia (B.C.).
  • David A Clifton
  • Gary S Collins
    Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Spiros Denaxas
    UCL Institute of Health Informatics and Farr Institute of Health Informatics Research, London, United Kingdom.
  • Alastair K Denniston
    Centre for Patient Reported Outcomes Research Institute of Applied Health Research University of Birmingham Birmingham Reino Unido Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, Reino Unido.
  • Livia Faes
    Moorfields Eye Hospital NHS Foundation Trust, London, UK; Eye Clinic, Cantonal Hospital of Lucerne, Lucerne, Switzerland.
  • Bart Geerts
    Healthplus.ai-R&D BV, Amsterdam, The Netherlands.
  • Mudathir Ibrahim
    Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Xiaoxuan Liu
    Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham Reino Unido Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, Reino Unido.
  • Bilal A Mateen
    Alan Turing Institute, Kings Cross, London, UK.
  • Piyush Mathur
    Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, 9500 Euclid Ave - E31, Cleveland, OH, 44195, USA.
  • Melissa D McCradden
    Division of Neurosurgery (McCradden, Baba, Saha, Boparai, Fadaiefard, Cusimano), St. Michael's Hospital, Unity Health Toronto; Dalla Lana School of Public Health (Cusimano), University of Toronto, Toronto, Ont. injuryprevention@smh.ca.
  • Lauren Morgan
    Morgan Human Systems Ltd, Shrewsbury, UK.
  • Johan Ordish
    Medicines and Healthcare Products Regulatory Agency, London, UK.
  • Campbell Rogers
    HeartFlow Inc., Redwood City, CA, USA.
  • Suchi Saria
    Department of Computer Science, Johns Hopkins University, Baltimore, MD.
  • Daniel S W Ting
    Singapore National Eye Center, Duke-National University of Singapore Medical School, Singapore 168751, Singapore; National Institutes of Health Research Biomedical Research Centre Biomedical Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Electronic address: daniel.ting.s.w@singhealth.com.sg.
  • Peter Watkinson
    Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Kadoorie Centre for Critical Care Research and Education, Oxford OX3 9DU, UK. Electronic address: peter.watkinson@ndcn.ox.ac.uk.
  • Wim Weber
    The BMJ, London, UK.
  • Peter Wheatstone
    School of Medicine, University of Leeds, Leeds, UK.
  • Peter McCulloch
    Nuffield Department of Surgical Science Level 6, John Radcliffe Hospital, Oxford OX3 9DU, UK. Electronic address: peter.mcculloch@nds.ox.ac.uk.

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