Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review
- PMID: 38670745
- PMCID: PMC11068159
- DOI: 10.1016/S2589-7500(24)00047-5
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 © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.
Conflict of interest statement
Declaration of interests 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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