Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

By Sebastian J. Vollmer, B. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, P. Jónsson, Sarah Cumbers, Adrian Jonas, K. McAllister, Puja Myles and 8 more. British medical journal, 2020

418 citations recorded by the source corpus.

Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/c8e469747bcf717633ba3758d82f5edf03f926e6

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Citations in the corpus

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “First, a de ﬁ nition of fairness should be chosen that corresponds with the AIPM ’ s intended use 16 .”
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (2022, neutral): “…AI-based diagnostic decision support systems 3 ; (2) an additional literature search about existing guidance for AI evaluation in clinical settings (search strategy available on the OSF 44 ); (3) literature recommended by Steering Group members 19,22,45–49 ; and (4) institutional documents 50–53 .”
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2023, neutral): “9 To have a real impact at scale, such guidelines for responsible and trustworthy AI must be obtained through wide consensus involving international and interdisciplinary experts.”