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. 2024 May 29;3(5):e0000514.
doi: 10.1371/journal.pdig.0000514. eCollection 2024 May.

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

Sarim Dawar Khan  1 , Zahra Hoodbhoy  1   2 , Mohummad Hassan Raza Raja  1 , Jee Young Kim  3 , Henry David Jeffry Hogg  4   5   6 , Afshan Anwar Ali Manji  1 , Freya Gulamali  3 , Alifia Hasan  3 , Asim Shaikh  1 , Salma Tajuddin  1 , Nida Saddaf Khan  1 , Manesh R Patel  7   8 , Suresh Balu  3 , Zainab Samad  1   9 , Mark P Sendak  3
Affiliations

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

Sarim Dawar Khan et al. PLOS Digit Health. .

Abstract

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.

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Conflict of interest statement

MPS is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc., KelaHealth, Inc, and Cohere-Med, Inc. MPS holds equity in Clinetic, Inc. MPS has received honorarium for a conference presentation from Roche. MPS is a board member of Machine Learning for Health Care, a non-profit that convenes an annual research conference. SB is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc. and Cohere-Med, Inc. SB holds equity in Clinetic, Inc.

Figures

Fig 1
Fig 1. PRISMA diagram.
Fig 2
Fig 2. Sankey Diagram showing distribution of themes.
See this image and copyright information in PMC

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