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人工智能干预的临床评估。

Clinical Evaluation of Artificial Intelligence-Enabled Interventions.

机构信息

University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.

Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom.

出版信息

Invest Ophthalmol Vis Sci. 2024 Aug 1;65(10):10. doi: 10.1167/iovs.65.10.10.

DOI:10.1167/iovs.65.10.10
PMID:39106058
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11309043/
Abstract

Artificial intelligence (AI) health technologies are increasingly available for use in real-world care. This emerging opportunity is accompanied by a need for decision makers and practitioners across healthcare systems to evaluate the safety and effectiveness of these interventions against the needs of their own setting. To meet this need, high-quality evidence regarding AI-enabled interventions must be made available, and decision makers in varying roles and settings must be empowered to evaluate that evidence within the context in which they work. This article summarizes good practices across four stages of evidence generation for AI health technologies: study design, study conduct, study reporting, and study appraisal.

摘要

人工智能（AI）健康技术在现实护理中的应用越来越广泛。这一新兴机会伴随着医疗保健系统中决策者和从业者的需求，需要评估这些干预措施对其自身环境的安全性和有效性。为了满足这一需求，必须提供有关人工智能支持的干预措施的高质量证据，并且必须赋予不同角色和环境中的决策者在其工作背景下评估该证据的能力。本文总结了人工智能健康技术在证据生成的四个阶段的良好实践：研究设计、研究实施、研究报告和研究评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e434/11309043/6009a72b98fc/iovs-65-10-10-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e434/11309043/6431dbf2aed5/iovs-65-10-10-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e434/11309043/6009a72b98fc/iovs-65-10-10-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e434/11309043/6431dbf2aed5/iovs-65-10-10-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e434/11309043/6009a72b98fc/iovs-65-10-10-f002.jpg

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本文引用的文献

1
Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).人工智能干预措施的综合健康经济评估报告标准 (CHEERS-AI)。
Value Health. 2024 Sep;27(9):1196-1205. doi: 10.1016/j.jval.2024.05.006. Epub 2024 May 23.
2
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.TRIPOD+AI 声明：报告使用回归或机器学习方法的临床预测模型的更新指南。
BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.
3
Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.
人工智能干预随机对照试验与 CONSORT-AI 报告指南的一致性。
Nat Commun. 2024 Feb 22;15(1):1619. doi: 10.1038/s41467-024-45355-3.
4
Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research.评估实施科学向临床人工智能的转化：一项定性研究的文献计量学分析
Front Health Serv. 2023 Jul 10;3:1161822. doi: 10.3389/frhs.2023.1161822. eCollection 2023.
5
Rams, hounds and white boxes: Investigating human-AI collaboration protocols in medical diagnosis.公羊、猎犬和白盒子：探索医学诊断中人机协作协议。
Artif Intell Med. 2023 Apr;138:102506. doi: 10.1016/j.artmed.2023.102506. Epub 2023 Feb 8.
6
Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.利益相关者对临床人工智能实施的观点：定性证据的系统评价。
J Med Internet Res. 2023 Jan 10;25:e39742. doi: 10.2196/39742.
7
Tackling bias in AI health datasets through the STANDING Together initiative.通过“携手共进”倡议应对人工智能健康数据集中的偏差问题。
Nat Med. 2022 Nov;28(11):2232-2233. doi: 10.1038/s41591-022-01987-w.
8
Assessment of Adherence to Reporting Guidelines by Commonly Used Clinical Prediction Models From a Single Vendor: A Systematic Review.评估单一供应商常用临床预测模型报告指南的依从性：系统评价。
JAMA Netw Open. 2022 Aug 1;5(8):e2227779. doi: 10.1001/jamanetworkopen.2022.27779.
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Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.人机协作是采用人工智能的关键：临床医生使用已部署机器学习系统的经验。
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