aceMultilingual Disparities in Large Language Model-Based Symptom Detection for Global Disease Surveillance: Evaluation Study
Background Symptom detection is essential in global disease surveillance to detect potential outbreaks, as symptoms are the first observable signs of infection. To reflect real-time ground truth conditions during pandemics, social media has emerged as a valuable data source. Moreover, effective digital disease surveillance systems must operate across diverse linguistic settings, and large language models (LLMs) have been shown to perform inconsistently across languages, tending to have lower performance in low-r…
GPT-5 detected symptoms less accurately in Southeast Asian and low-resource languages than in others, which could make social media based disease surveillance uneven across regions.
The evaluation used a pseudo-social media dataset and a single model, GPT-5, so the findings may not reflect real social media posts or other systems.
Evidence
- Peer-reviewedJMIR AI2026-10-09
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Truvace Impact Record TRV-2026-1665, v1: “Multilingual Disparities in Large Language Model-Based Symptom Detection for Global Disease Surveillance: Evaluation Study.” Truvace, 2026-10-17. /record/TRV-2026-1665 (accessed at citation time). sha256 f9b50cf4434a6aa0…
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