%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 8
%P e20007
%T Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine
%A Michelson,Matthew
%A Chow,Tiffany
%A Martin,Neil A
%A Ross,Mike
%A Tee Qiao Ying,Amelia
%A Minton,Steven
%+ Evid Science, 2361 Rosencrans Ave Ste 348, El Segundo, CA, 90245-4929, United States, 1 626 765 1903, mmichelson@evidscience.com
%K meta-analysis
%K rapid meta-analysis
%K artificial intelligence
%K drug
%K analysis
%K hydroxychloroquine
%K toxic
%K COVID-19
%K treatment
%K side effect
%K ocular
%K eye
%D 2020
%7 17.8.2020
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 32804086
%R 10.2196/20007
%U http://www.jmir.org/2020/8/e20007/
%U https://doi.org/10.2196/20007
%U http://www.ncbi.nlm.nih.gov/pubmed/32804086