Accuracy of Online Symptom-Assessment Applications, Large Language Models, and Laypeople for Self-Triage Decisions: A Systematic Review

Abstract

Abstract Symptom-Assessment Application (SAAs, e.g., NHS 111 online) that assist medical laypeople in deciding if and where to seek care ( self-triage ) are gaining popularity and their accuracy has been examined in numerous studies. With the public release of Large Language Models (LLMs, e.g., ChatGPT), their use in such decision-making processes is growing as well. However, there is currently no comprehensive evidence synthesis for LLMs, and no review has contextualized the accuracy of SAAs and LLMs relative to the accuracy of their users. Thus, this systematic review evaluates the self-triage accuracy of both SAAs and LLMs and compares them to the accuracy of medical laypeople. A total of 1549 studies were screened, with 19 included in the final analysis. The self-triage accuracy of SAAs was found to be moderate but highly variable (11.5 – 90.0%), while the accuracy of LLMs (57.8 – 76.0%) and laypeople (47.3 – 62.4%) was moderate with low variability. Despite some published recommendations to standardize evaluation methodologies, there remains considerable heterogeneity among studies. The use of SAAs should not be universally recommended or discouraged; rather, their utility should be assessed based on the specific use case and tool under consideration.

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