Department of Diagnostic Radiology, Kingston Health Sciences Centre, Kingston General Hospital, Kingston, ON, Canada.
Department of Medical Imaging, 7938University of Toronto, Toronto, ON, Canada.
Can Assoc Radiol J. 2023 May;74(2):334-342. doi: 10.1177/08465371221134056. Epub 2022 Oct 27.
To establish reporting adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) in diagnostic accuracy AI studies with the highest Altmetric Attention Scores (AAS), and to compare completeness of reporting between peer-reviewed manuscripts and preprints. MEDLINE, EMBASE, arXiv, bioRxiv, and medRxiv were retrospectively searched for 100 diagnostic accuracy medical imaging AI studies in peer-reviewed journals and preprint platforms with the highest AAS since the release of CLAIM to June 24, 2021. Studies were evaluated for adherence to the 42-item CLAIM checklist with comparison between peer-reviewed manuscripts and preprints. The impact of additional factors was explored including body region, models on COVID-19 diagnosis and journal impact factor. Median CLAIM adherence was 48% (20/42). The median CLAIM score of manuscripts published in peer-reviewed journals was higher than preprints, 57% (24/42) vs 40% (16/42), < .0001. Chest radiology was the body region with the least complete reporting ( = .0352), with manuscripts on COVID-19 less complete than others (43% vs 54%, = .0002). For studies published in peer-reviewed journals with an impact factor, the CLAIM score correlated with impact factor, rho = 0.43, = .0040. Completeness of reporting based on CLAIM score had a positive correlation with a study's AAS, rho = 0.68, < .0001. Overall reporting adherence to CLAIM is low in imaging diagnostic accuracy AI studies with the highest AAS, with preprints reporting fewer study details than peer-reviewed manuscripts. Improved CLAIM adherence could promote adoption of AI into clinical practice and facilitate investigators building upon prior works.
为了确定在具有最高 Altmetric 关注得分 (AAS) 的诊断准确性人工智能研究中，使用人工智能进行医学成像的清单 (CLAIM) 的报告是否符合要求，并比较同行评议的手稿和预印本之间报告的完整性。对 MEDLINE、EMBASE、arXiv、bioRxiv 和 medRxiv 进行了回顾性检索，以获取自 CLAIM 发布以来至 2021 年 6 月 24 日在具有最高 AAS 的同行评议期刊和预印本平台上发表的 100 项诊断准确性医学成像 AI 研究。评估了这些研究对 CLAIM 42 项清单的遵守情况，并比较了同行评议的手稿和预印本。还探讨了其他因素的影响，包括身体部位、COVID-19 诊断模型和期刊影响因子。CLAIM 遵守的中位数为 48%（20/42）。发表在同行评议期刊上的手稿的 CLAIM 评分高于预印本，分别为 57%（24/42）和 40%（16/42），<.0001。胸部放射学的报告完整性最差（=.0352），COVID-19 相关的手稿比其他研究更不完整（43%比 54%，=.0002）。对于发表在具有影响因子的同行评议期刊上的研究，CLAIM 评分与影响因子相关，rho = 0.43，=.0040。基于 CLAIM 评分的报告完整性与研究的 AAS 呈正相关，rho = 0.68，<.0001。在具有最高 AAS 的成像诊断准确性人工智能研究中，CLAIM 的总体报告遵守情况较低，预印本报告的研究细节比同行评议的手稿少。提高 CLAIM 的遵守度可以促进 AI 在临床实践中的应用，并有助于研究人员在先前工作的基础上进行研究。
