JMIR Publications

ABSTRACT

Background:

When introducing artificial intelligence (AI) into clinical care, one of the main objectives is to improve workflow efficiency because AI-based solutions are expected to control or support routine tasks.

Objective:

This study sought to synthesize the current knowledge base on how the use of AI technologies for diagnostic tasks affects efficiency and what facilitators or barriers moderating the impact of AI implementation have been reported.

Methods:

In this is a systematic literature review, comprehensive literature searches will be performed in relevant electronic databases, including PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, CINAHL, and CENTRAL. Studies in English and German published between 2000 and 2021 will be included. The following inclusion criteria will be applied: empirical studies targeting the workflow integration or adoption of AI solutions for clinical diagnostics in a hospital care setting and studies targeting AI-based software for clinical diagnostics to allow comparability between AI solutions. The efficiency outcomes of interest include workflow adaptation, time to complete tasks, and workload. Two reviewers will independently screen all retrieved records, full-text articles, and extract data. The study’s methodological quality will be appraised using suitable tools. The findings will be described qualitatively, and a meta-analysis will be performed, if possible. Furthermore, a narrative synthesis approach that focuses on work system factors affecting the integration of AI technologies reported in eligible studies will be adopted.

Results:

This systemaThis systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings.tic review is anticipated to begin in August 2022 and be completed in April 2023.

Conclusions:

This systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings. Clinical Trial: Registration on PROSPERO, trial registration ID will be added after peer review