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

\ua9 2024 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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

Unlocking the potential of qualitative research for the implementation of artificial intelligence-enabled healthcare

 Artificial intelligence (AI)-enabled clinical decision support tools (CDSTs) are complicated technologies, which form the basis of complex AI-enabled healthcare interventions. Research of AI-enabled CDSTs has proliferated, with 57,844 model development studies and 5,073 comparative or real-world evaluation studies readily identifiable on PubMed at the time of writing (1). Despite this proliferation of evidence, a notable translational gap persists with little real-world implementation of AI-enabled healthcare interventions (2). While research communities have acknowledged the value and importance of studying AI implementation in real-world clinical settings, there is limited evidence on how to translate the potential of AI into everyday healthcare practices. This persistent translational failure is multifactorial, but there is clear opportunity for impact from the research community if they can deliver the evidence that healthcare systems’ decision makers need to fully evaluate complex interventions such as those involving AI-enabled CDSTs (2). This need for a holistic evidence base exists because AI-enabled CDSTs cannot be considered as inert and isolated technologies, but as components of a complex system which shape and are shaped by the adopters and organisations which enable their impact. The complexity surrounding the clinical implementation of AI tools and applications requires therefore to better understand the interplay between agency, social processes, and contextual conditions shaping implementation. Qualitative research provides a valuable approach to study AI implementation because it allows research communities to explore the interplay between social processes and contextual factors shaping the implementation of change (3). Qualitative research can also surface how these factors may be anticipated or modified to support judicious and successful implementation efforts across varied sociotechnical contexts. In so doing, it helps to answer complex questions such as how and why efforts to implement best practices may succeed or fail, and how patients and providers experience and make decisions in care (4). </p

'Keep telling until someone listens': understanding prevention concepts in children's picture books dealing with child sexual abuse

Children’s picture books dealing with the topic of child sexual abuse appeared in the 1980s with the aim of addressing the need for age-appropriate texts to teach sexual abuse prevention concepts and to provide support for young children who may be at risk of or have already experienced sexual abuse. Despite the apparent potential of children’s picture books to convey child sexual abuse prevention concepts, very few studies have addressed the topic of child sexual abuse in children’s literature. This article critically examines a selection of 15 picture books (published in the US, Canada and Australia) for children aged 3–8 years dealing with this theme. It makes use of an established set of evaluative criteria to conduct an audit of the books’ content and applies techniques of literary discourse analysis to explain how these picture books satisfy criteria for child sexual abuse prevention. The analysis is used as a way to understand the discourses available to readers, both adults and children, on the topic of child sexual abuse. Key themes in the books include children’s empowerment and agency, and the need for persistence and hope