@Article{info:doi/10.2196/20007,
author="Michelson, Matthew
and Chow, Tiffany
and Martin, Neil A
and Ross, Mike
and Tee Qiao Ying, Amelia
and Minton, Steven",
title="Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="17",
volume="22",
number="8",
pages="e20007",
keywords="meta-analysis; rapid meta-analysis; artificial intelligence; drug; analysis; hydroxychloroquine; toxic; COVID-19; treatment; side effect; 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="http://www.jmir.org/2020/8/e20007/",
url="https://doi.org/10.2196/20007",
url="http://www.ncbi.nlm.nih.gov/pubmed/32804086"
}