  The potential presented by Artificial Intelligence (AI) for healthcare has
long been recognised by the technical community. More recently, this potential
has been recognised by policymakers, resulting in considerable public and
private investment in the development of AI for healthcare across the globe.
Despite this, excepting limited success stories, real-world implementation of
AI systems into front-line healthcare has been limited. There are numerous
reasons for this, but a main contributory factor is the lack of internationally
accepted, or formalised, regulatory standards to assess AI safety and impact
and effectiveness. This is a well-recognised problem with numerous ongoing
research and policy projects to overcome it. Our intention here is to
contribute to this problem-solving effort by seeking to set out a minimally
viable framework for evaluating the safety, acceptability and efficacy of AI
systems for healthcare. We do this by conducting a systematic search across
Scopus, PubMed and Google Scholar to identify all the relevant literature
published between January 1970 and November 2020 related to the evaluation of:
output performance; efficacy; and real-world use of AI systems, and
synthesising the key themes according to the stages of evaluation: pre-clinical
(theoretical phase); exploratory phase; definitive phase; and post-market
surveillance phase (monitoring). The result is a framework to guide AI system
developers, policymakers, and regulators through a sufficient evaluation of an
AI system designed for use in healthcare.
