TY  - JOUR
AU  - Michelson, Matthew
AU  - Chow, Tiffany
AU  - Martin, Neil A
AU  - Ross, Mike
AU  - Tee Qiao Ying, Amelia
AU  - Minton, Steven
PY  - 2020
DA  - 2020/8/17
TI  - Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine
JO  - J Med Internet Res
SP  - e20007
VL  - 22
IS  - 8
KW  - meta-analysis
KW  - rapid meta-analysis
KW  - artificial intelligence
KW  - drug
KW  - analysis
KW  - hydroxychloroquine
KW  - toxic
KW  - COVID-19
KW  - treatment
KW  - side effect
KW  - ocular
KW  - eye
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2020/8/e20007/
UR  - https://doi.org/10.2196/20007
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32804086
DO  - 10.2196/20007
ID  - info:doi/10.2196/20007
ER  -