Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.Author summary: The use of artificial intelligence (AI) tools has seen exponential growth in multiple industries, over the past few years. Despite this, the implementation of these tools in healthcare settings is lagging with less than 600 AI tools approved by the United States Food and Drug Administration and fewer job AI related job postings in healthcare as compared to other industries. In this systematic review, we tried to organize and synthesize data and themes from published literature regarding key aspects of AI tool implementation; namely procurement, integration, monitoring and evaluation and map the extracted themes on to the Plan-Do-Study-Act framework. We found that currently the majority of literature on AI implementation in healthcare settings focuses on the “Plan” and “Study” domains with considerably less emphasis on the “Do” and “Act” domains. This is perhaps the reason why experts currently cite the implementation of AI tools in healthcare settings as challenging. Furthermore, the current AI healthcare landscape has poor representation from low and lower-middle-income countries. To ensure, the healthcare industry is able to implement AI tool into clinical workforce, across a variety of settings globally, we call for diverse and inclusive collaborations, coupled with further research targeted on the under-investigated stages of AI implementation.