He Zixuan, Yang Lan, Li Xiaofan, Du Jian
Institute of Medical Technology, Health Science Center of Peking University, Beijing, China.
National Institute of Health Data Science, Peking University, Beijing, China.
EClinicalMedicine. 2025 Jan 20;80:103066. doi: 10.1016/j.eclinm.2024.103066. eCollection 2025 Feb.
Complete and unbiased reporting of clinical trial results is essential for evaluating medical advances, yet publication bias and reporting discrepancies in research on the clinical application of artificial intelligence (AI) remain unknown.
We conducted a comprehensive search of research publications and clinical trial registries focused on the application of AI in healthcare. Our search included publications in Dimensions.ai and pre-registered records from ClinicalTrials.gov and the EU Clinical Trials Registry before 31 December 2023. We linked registered trials to their corresponding publications, analysed the registration, reporting and different dissemination patterns of results, identified discrepancies between clinical trial registries and published literature, and assessed the use of these results in secondary research.
We identified 28,248 publications related to the use of AI in clinical settings and found 1863 publications that included a clinical trial registration ID. The clinical trial registry search identified 3710 trials evaluating the use of AI in clinical settings, of which 1106 trials are completed, yet only 101 trials have published results. By linking the trials to their corresponding publications, we found that 26 trials had results available from both registries and publications. There were more results in trial registries than in articles, but researchers showed a clear preference for rapid dissemination of results through peer-reviewed articles (37.6% published within one year) over trial registries (15.8%). Discrepancies and omissions of results were common, and no complete agreement was observed between the two sources. Selective reporting of publications occurred in 53.6% of cases, and the underestimation of the incidence of adverse events is alarming.
This research uncovers concerns with the registration and reporting of AI clinical trial results. While trial registries and publications serve distinct yet complementary roles in disseminating research findings, discrepancies between them may undermine the reliability of the evidence. We emphasise adherence to guidelines that promote transparency and standardisation of reporting, especially for investigator-initiated trials (IITs).
The authors declare no source of funding.
完整且无偏倚地报告临床试验结果对于评估医学进展至关重要,但人工智能(AI)临床应用研究中的发表偏倚和报告差异仍不为人知。
我们对专注于AI在医疗保健中应用的研究出版物和临床试验注册库进行了全面搜索。我们的搜索包括Dimensions.ai上的出版物以及2023年12月31日前ClinicalTrials.gov和欧盟临床试验注册库中的预注册记录。我们将注册试验与其相应的出版物进行关联,分析结果的注册、报告和不同传播模式,识别临床试验注册库与已发表文献之间的差异,并评估这些结果在二次研究中的使用情况。
我们识别出28248篇与AI在临床环境中的使用相关的出版物,并发现1863篇包含临床试验注册ID的出版物。临床试验注册库搜索识别出3710项评估AI在临床环境中使用的试验,其中1106项试验已完成,但只有101项试验发表了结果。通过将试验与其相应的出版物进行关联,我们发现26项试验在注册库和出版物中均有结果。试验注册库中的结果比文章中的多,但研究人员明显更倾向于通过同行评审文章(37.6%在一年内发表)而非试验注册库(15.8%)快速传播结果。结果的差异和遗漏很常见,两个来源之间未观察到完全一致的情况。53.6%的案例中存在出版物的选择性报告,不良事件发生率的低估令人担忧。
本研究揭示了对AI临床试验结果的注册和报告的担忧。虽然试验注册库和出版物在传播研究结果方面发挥着不同但互补的作用,但它们之间的差异可能会削弱证据可靠性。我们强调遵守促进报告透明度和标准化的指南,特别是对于研究者发起的试验(IIT)。
作者声明无资金来源。