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The potential presented by Artificial Intelligence (AI) for healthcare haslong been recognised by the technical community. More recently, this potentialhas been recognised by policymakers, resulting in considerable public andprivate investment in the development of AI for healthcare across the globe.Despite this, excepting limited success stories, real-world implementation ofAI systems into front-line healthcare has been limited. There are numerousreasons for this, but a main contributory factor is the lack of internationallyaccepted, or formalised, regulatory standards to assess AI safety and impactand effectiveness. This is a well-recognised problem with numerous ongoingresearch and policy projects to overcome it. Our intention here is tocontribute to this problem-solving effort by seeking to set out a minimallyviable framework for evaluating the safety, acceptability and efficacy of AIsystems for healthcare. We do this by conducting a systematic search acrossScopus, PubMed and Google Scholar to identify all the relevant literaturepublished between January 1970 and November 2020 related to the evaluation of:output performance; efficacy; and real-world use of AI systems, andsynthesising the key themes according to the stages of evaluation: pre-clinical(theoretical phase); exploratory phase; definitive phase; and post-marketsurveillance phase (monitoring). The result is a framework to guide AI systemdevelopers, policymakers, and regulators through a sufficient evaluation of anAI system designed for use in healthcare.

Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis