PDF(Pubmed)
Abstract：
High-quality research is essential in guiding evidence-based care, and should be reported in a way that is reproducible, transparent and where appropriate, provide sufficient detail for inclusion in future meta-analyses. Reporting guidelines for various study designs have been widely used for clinical (and preclinical) studies, consisting of checklists with a minimum set of points for inclusion. With the recent rise in volume of research using artificial intelligence (AI), additional factors need to be evaluated, which do not neatly conform to traditional reporting guidelines (eg, details relating to technical algorithm development). In this review, reporting guidelines are highlighted to promote awareness of essential content required for studies evaluating AI interventions in healthcare. These include published and in progress extensions to well-known reporting guidelines such as Standard Protocol Items: Recommendations for Interventional Trials-AI (study protocols), Consolidated Standards of Reporting Trials-AI (randomised controlled trials), Standards for Reporting of Diagnostic Accuracy Studies-AI (diagnostic accuracy studies) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-AI (prediction model studies). Additionally there are a number of guidelines that consider AI for health interventions more generally (eg, Checklist for Artificial Intelligence in Medical Imaging (CLAIM), minimum information (MI)-CLAIM, MI for Medical AI Reporting) or address a specific element such as the \'learning curve\' (Developmental and Exploratory Clinical Investigation of Decision-AI) . Economic evaluation of AI health interventions is not currently addressed, and may benefit from extension to an existing guideline. In the face of a rapid influx of studies of AI health interventions, reporting guidelines help ensure that investigators and those appraising studies consider both the well-recognised elements of good study design and reporting, while also adequately addressing new challenges posed by AI-specific elements.
摘要：
高质量的研究对于指导循证护理至关重要，并且应该以可重复的方式报告，透明，在适当的情况下，为纳入未来的荟萃分析提供足够的细节。各种研究设计的报告指南已广泛用于临床（和临床前）研究，由包含最小点集的清单组成。随着最近使用人工智能(AI)的研究数量的增加，需要评估其他因素，不完全符合传统的报告准则(例如，与技术算法开发相关的详细信息)。在这次审查中,强调报告指南，以提高对评估医疗保健中AI干预研究所需的基本内容的认识。其中包括已发布和正在进行的对众所周知的报告指南的扩展，例如标准协议项目：对介入试验的建议-AI（研究协议），报告试验综合标准-AI（随机对照试验），诊断准确性研究报告标准-AI（诊断准确性研究）和个人预后或诊断多变量预测模型的透明报告-AI（预测模型研究）。此外，还有许多指南更广泛地考虑将人工智能用于健康干预(例如，医学影像人工智能清单(CLAIM)，最小信息(MI)-索赔，用于医疗AI报告的MI）或解决特定元素，例如“学习曲线”（决策AI的发展和探索性临床研究）。人工智能健康干预措施的经济评估目前尚未解决，并可能受益于现有准则的扩展。面对AI健康干预研究的迅速涌入，报告指南有助于确保研究人员和评估研究人员同时考虑良好研究设计和报告的公认要素，同时也充分应对AI特定元素带来的新挑战。
