How to Evaluate the Accuracy of Online and AI-Based Symptom Checkers: A Standardized Methodological Framework

Abstract

Online and AI-based symptom checkers are applications that assist medical laypeople in diagnosing their symptoms and determining which course of action to take. When evaluating these tools, previous studies primarily used an approach introduced a decade ago that lacked any type of quality control. Numerous studies have criticized this approach, and several empirical studies have sought to improve specific aspects of evaluations. However, even after a decade, a high-quality methodological framework for standardizing the evaluation of symptom checkers remains missing. This article synthesizes empirical studies to outline a framework for standardized evaluations based on representative case selection, an externally and internally valid evaluation design, and metrics that increase cross-study comparability. This approach is backed up by several open-access resources to facilitate implementation. Ultimately, this approach should enhance the quality and comparability of future evaluations of online and AI-based symptom checkers to enable meta-analyses and help stakeholders make more informed decisions.

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