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

被引:0
|
作者
Han, Ryan [1 ,2 ,3 ]
Acosta, Julian N. [4 ,5 ]
Shakeri, Zahra [6 ]
Ioannidis, John P. A. [7 ,8 ]
Topol, Eric J. [9 ]
Rajpurkar, Pranav [1 ]
机构
[1] Harvard Med Sch, Dept Biomed Informat, Boston, MA USA
[2] Stanford Univ, Dept Comp Sci, Stanford, CA USA
[3] Univ Calif Angeles, Caltech Med ScientistTraining Program, Los Angeles, CA USA
[4] Yale Sch Med, Dept Neurol, New Haven, CT USA
[5] Rad AI, San Francisco, CA USA
[6] Univ Toronto, Inst Hlth Policy Management & Evaluat, Dalla Lana Sch Publ Hlth, Toronto, ON, Canada
[7] Stanford Univ, Stanford Prevent Res Ctr, Dept Med, Stanford, CA USA
[8] Stanford Univ, Meta Res Innovat Ctr Stanford, Stanford, CA USA
[9] Scripps Res, Scripps Res Translat Inst, La Jolla, CA 92037 USA
来源
LANCET DIGITAL HEALTH | 2024年 / 6卷 / 05期
关键词
MULTICENTER; ALGORITHM; HEALTH; CARE;
D O I
暂无
中图分类号
R-058 [];
学科分类号
摘要
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.
引用
收藏
页码:e367 / e373
页数:7
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