Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA.
Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA.
Lancet Digit Health. 2024 May;6(5):e367-e373. doi: 10.1016/S2589-7500(24)00047-5.
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.
这篇关于人工智能（AI）在临床实践中随机对照试验的范围综述表明，人们对 AI 在各个临床专业和地点的应用兴趣日益浓厚。美国和中国在试验数量上处于领先地位，重点是用于医学成像的深度学习系统，特别是在胃肠病学和放射学领域。大多数试验（86 项中的 70 项[81%]）报告了积极的主要终点，主要与诊断产量或性能有关；然而，大多数是单中心试验，很少有关于人口统计学的报告，以及关于运营效率的不同报告，这引起了对这些结果的普遍性和实用性的关注。尽管结果很有前景，但考虑到发表偏倚的可能性，以及需要更全面的研究，包括多中心试验、不同的结果衡量标准和改进的报告标准，这一点至关重要。未来的 AI 试验应优先考虑患者相关的结果，以全面了解 AI 在医疗保健中的真实效果和局限性。
