@Article{info:doi/10.2196/20007，作者=“Michelson, Matthew and Chow, Tiffany and Martin, Neil A and Ross, Mike and Tee Qiao Ying, Amelia and Minton, Steven”，标题=“人工智能快速元分析:羟基氯quine眼毒性的案例研究”，期刊=“J Med Internet Res”，年=“2020”，月=“8”，日=“17”，卷=“22”，号=“8”，页=“e20007”，关键词=“元分析”;快速分析;人工智能;药物;分析;羟氯喹;有毒的;COVID-19;治疗;副作用; ocular; eye", abstract="Background: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. Objective: We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. Methods: The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. Results: By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4{\%} (95{\%} CI 1.11{\%}-9.96{\%}). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. Conclusions: We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis. ", issn="1438-8871", doi="10.2196/20007", url="//www.mybigtv.com/2020/8/e20007/", url="https://doi.org/10.2196/20007", url="http://www.ncbi.nlm.nih.gov/pubmed/32804086" }