Abstract
The application of new artificial intelligence (AI) discoveries is transforming healthcare research. However, the standards of reporting are variable in this still evolving field, leading to potential research waste. The aim of this work is to highlight resources and reporting guidelines available to researchers working in computational pathology. The EQUATOR Network library of reporting guidelines and extensions was systematically searched up to August 2022 to identify applicable resources. Inclusion and exclusion criteria were used and guidance was screened for utility at different stages of research and for a range of study types. Items were compiled to create a summary for easy identification of useful resources and guidance. Over 70 published resources applicable to pathology AI research were identified. Guidelines were divided into key categories, reflecting current study types and target areas for AI research: Literature & Research Priorities, Discovery, Clinical Trial, Implementation and Post-Implementation & Guidelines. Guidelines useful at multiple stages of research and those currently in development were also highlighted. Summary tables with links to guidelines for these groups were developed, to assist those working in cancer AI research with complete reporting of research. Issues with replication and research waste are recognised problems in AI research. Reporting guidelines can be used as templates to ensure the essential information needed to replicate research is included within journal articles and abstracts. Reporting guidelines are available and useful for many study types, but greater awareness is needed to encourage researchers to utilise them and for journals to adopt them. This review and summary of resources highlights guidance to researchers, aiming to improve completeness of reporting.
Abstract (translated)
新人工智能发现的应用正在改变医疗研究。然而,这个仍在不断发展的领域的标准报告要求是不确定的,导致可能存在的研究浪费。这项工作的目标是强调可供计算病理学研究人员使用的资源以及报告指南。截至2022年8月,我们对EQUATOR网络中的报告指南和扩展进行了全面系统地搜索,以确定可用的资源。使用 inclusion and exclusion 标准,对指南在研究不同阶段的实用性进行了筛选,并针对多种研究类型进行了分类。项目被整理成 summary,以方便识别有用的资源和指南。超过70个适用于病理学人工智能研究的公开资源被识别出来。指南被分为关键类别,反映了当前研究类型和人工智能研究的目标领域:文献与研究重点、发现、临床试验、实施和实施后指南。指南在多个研究阶段和目前正在开发的研究中也非常有用。此外,还强调了这些组中的指南 summary 表格,以帮助那些在癌症人工智能研究中进行完整报告的研究人员。复制和研究浪费是人工智能研究中的常见问题。报告指南可以作为模板,以确保包含复制研究所需的关键信息,在期刊文章和摘要中包含。报告指南对于许多研究类型是可用的和有用的,但需要更多的意识来鼓励研究人员利用它们,并促使期刊采用它们。本综述和指南强调向研究人员提供指导,以改善报告的完整性。
URL
https://arxiv.org/abs/2301.09985
