Published May 2024 | Version Published
Journal Article Open

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review

Abstract

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.

    Copyright and License

    © 2024 The Author(s). Published by Elsevier Under a Creative Commons license.

    Acknowledgement

    RH pursued this work while supported by the Summer Institute for Biomedical Informatics at Harvard Medical School.

    Contributions

    RH and PR conceptualised the scoping review. JPAI, PR, and EJT supervised the scoping review. RH, JPAI, and PR contributed to the design of the scoping review. RH, JNA, and PR did the screening of the search results and data extraction. RH drafted the manuscript. ZS contributed to the presentation of data. All authors had access to the data, interpreted the analyses, and critically revised and edited the manuscript.

    Conflict of Interest

    EJT receives funding from the National Center for Advancing Translational Sciences/National Institutes of Health (grant number UL1TR002550). JNA is an employee of Rad AI, outside of the submitted work. RH receives funding from the National Institute of General Medical Sciences (grant number T32 GM008042), and was formerly employed at Quadrant Health, outside of the submitted work. All other authors declare no competing interests.

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    Additional details

    Identifiers

    Funding

    National Institutes of Health
    UL1TR002550
    National Institutes of Health
    NIH Predoctoral Fellowship T32 GM008042