acePerformance evaluation of five major large language models in tuberculosis Q&A systems: A multidimensional assessment of readability, quality, and reliability
Background: Pulmonary tuberculosis (TB) is a chronic infectious disease that burdens patients and public health systems. Limited reach of traditional education and uneven online information may undermine patients' understanding, adherence, and trust. Large language models (LLMs) show promise for TB health education, but systematic evaluation is lacking. Objective: To evaluate five large language models in pulmonary tuberculosis Q&A (Question and Answer) scenarios and examine the effects of different large langua…
In a 20-question TB Q&A test generating 100 responses, GPT-5 produced the most suitable patient-education texts as measured by C-PEMAT-P among five LLMs.
The same five LLMs showed significant differences on several readability indices for TB education texts, creating uneven reading difficulty that may undermine patient understanding and adherence.
Evaluation limited to five models and 20 physician-assisted questions without patient-reported outcomes, requiring broader testing across models, diseases, and real patient use.
Evidence
- Peer-reviewedDIGITAL HEALTH2026-01-01
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Truvace Impact Record TRV-2026-0326, v1: “Performance evaluation of five major large language models in tuberculosis Q&A systems: A multidimensional assessment of readability, quality, and reliability.” Truvace, 2026-07-20. /record/TRV-2026-0326 (accessed at citation time). sha256 397115fc536c270e…
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