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Unveiling the black box: imperative for explainable AI in cardiovascular disease prevention.

作者信息

Wu Yanyi, Lin Chenghua

机构信息

School of Public Affairs, Zhejiang University, Hangzhou 310058, China.

Institute of China's Science, Technology and Policy, Zhejiang University, Hangzhou 310058, China.

出版信息

Lancet Reg Health West Pac. 2024 Jul 13;48:101145. doi: 10.1016/j.lanwpc.2024.101145. eCollection 2024 Jul.

DOI:10.1016/j.lanwpc.2024.101145
PMID:39104749
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11298886/
Abstract
摘要

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本文引用的文献

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Incorporating AI into cardiovascular diseases prevention-insights from Singapore.将人工智能融入心血管疾病预防——来自新加坡的见解
Lancet Reg Health West Pac. 2024 May 27;48:101102. doi: 10.1016/j.lanwpc.2024.101102. eCollection 2024 Jul.
2
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。
NPJ Digit Med. 2022 Jul 21;5(1):97. doi: 10.1038/s41746-022-00597-7.
3
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.人工智能在医疗保健中的可解释性:多学科视角。
BMC Med Inform Decis Mak. 2020 Nov 30;20(1):310. doi: 10.1186/s12911-020-01332-6.
4
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.可解释人工智能(XAI)研究综述:迈向医学 XAI
IEEE Trans Neural Netw Learn Syst. 2021 Nov;32(11):4793-4813. doi: 10.1109/TNNLS.2020.3027314. Epub 2021 Oct 27.
5
The four dimensions of contestable AI diagnostics - A patient-centric approach to explainable AI.可争辩 AI 诊断的四个维度 - 以患者为中心的可解释 AI 方法。
Artif Intell Med. 2020 Jul;107:101901. doi: 10.1016/j.artmed.2020.101901. Epub 2020 Jun 9.