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医学领域中评估人工智能研究的系统评价呈指数级增长：挑战与机遇并存。

Exponential growth of systematic reviews assessing artificial intelligence studies in medicine: challenges and opportunities.

机构信息

Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.

German Federal Institute for Risk Assessment, Berlin, Germany.

出版信息

Syst Rev. 2022 Jun 28;11(1):132. doi: 10.1186/s13643-022-01984-7.

DOI:10.1186/s13643-022-01984-7
PMID:35761303
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9238033/
Abstract

The evidence-based medicine (EBM) movement is stepping up its efforts to assess medical artificial intelligence (AI) and data science studies. Since 2017, there has been a marked increase in the number of published systematic reviews that assess medical AI studies. Increasingly, data from observational studies are used in systematic reviews of medical AI studies. Assessment of risk of bias is especially important in medical AI studies to detect possible "AI bias".

摘要

循证医学（EBM）运动正在加大力度评估医学人工智能（AI）和数据科学研究。自 2017 年以来，评估医学 AI 研究的已发表系统评价数量显著增加。越来越多的观察性研究数据被用于医学 AI 研究的系统评价。在医学 AI 研究中，评估偏倚风险尤其重要，以发现可能的“AI 偏差”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68fb/9238033/6b0bed9d403d/13643_2022_1984_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68fb/9238033/7280032855cf/13643_2022_1984_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68fb/9238033/6b0bed9d403d/13643_2022_1984_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68fb/9238033/7280032855cf/13643_2022_1984_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68fb/9238033/6b0bed9d403d/13643_2022_1984_Fig2_HTML.jpg

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