背景:尽管人工智能(AI)和机器学习(ML)具有彻底改变医疗保健的潜力,临床决策支持工具,本文称为医学建模软件(MMS),尚未实现预期的好处。一个提出的障碍是人工智能翻译中公认的差距。这些差距部分源于支持MMS透明文档的流程和资源的分散。因此,缺乏透明的报告阻碍了提供证据来支持MMS在临床实践中的实施,从而成为软件从研究环境到临床实践的成功翻译的实质性障碍。
目的:本研究旨在对基于AI和ML的MMS文档实践的现状进行分析,并阐明文档在促进将伦理和可解释的MMS转化为临床工作流程方面的功能。
方法:根据PRISMA-ScR(系统审查的首选报告项目和范围审查的Meta分析扩展)指南进行范围审查。PubMed使用医学主题词AI的关键概念进行搜索,ML,伦理考虑,和可解释性,以识别详细说明基于AI和ML的MMS文档的出版物,除了雪球采样选定的参考列表。要包括未明确标记为隐式文档实践的可能性,我们没有使用文档作为关键概念,而是作为纳入标准。由1位作者进行了2阶段筛选过程(标题和摘要筛选以及全文审查)。数据提取模板用于记录与出版物相关的信息;开发道德和可解释的MMS的障碍;可用标准,法规,框架,或与文档相关的治理策略;以及符合纳入标准的论文的文档建议。
结果:在检索到的115篇论文中,21篇(18.3%)论文符合纳入要求。在基于AI和ML的MMS文档和翻译的背景下研究了道德和可解释性。综合了详细说明当前状态和挑战的数据以及对未来研究的建议。定义当前状态和需要彻底审查的挑战的值得注意的主题包括偏见,问责制,治理,和可解释性。文献中确定的解决当前障碍的建议要求对MMS进行积极评估,多学科合作,遵守调查和验证协议,透明度和可追溯性要求,以及指导标准和框架,以增强文档工作并支持基于AI和ML的MMS的翻译。
结论:解决翻译障碍对于MMS实现期望至关重要,包括在这次范围界定审查中发现的与偏见有关的障碍,问责制,治理,和可解释性。我们的研究结果表明,透明的战略文件,调整翻译科学和监管科学,将通过协调沟通和报告以及减少翻译障碍来支持彩信的翻译,从而进一步采用彩信。
BACKGROUND: Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice.
OBJECTIVE: This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows.
METHODS: A scoping
review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text
review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria.
RESULTS: Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough
review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS.
CONCLUSIONS: Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping
review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.