Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine

Michelson, Matthew; Chow, Tiffany; Martin, Neil A; Ross, Mike; Tee Qiao Ying, Amelia; Minton, Steven

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.