Evaluating Chinese large language models for HIV/AIDS health information: a multidimensional comparative study of response quality.

Background: Chinese large language models (LLMs) are increasingly used for HIV/AIDS health information, yet their multidimensional performance remains unevaluated. This study systematically assessed five mainstream Chinese LLMs across accuracy, caring, completeness, understandability, and actionability. Methods: A cross-sectional evaluation was conducted using 50 standardized HIV/AIDS questions. Each model's responses were rated by three independent experts on five dimensions, including adapted PEMAT-based items. Latent profile analysis (LPA) and partial correlation network analysis were employed to identify quality typologies and interdimensional dependencies. Chi-square tests examined associations between profile membership and model identity or question domain. Results: The four general-purpose LLMs showed high accuracy (mean > 4.5/5) and completeness (86%-98% full marks), with no significant differences among them (all P-FDR > 0.05). HuatuoGPT-II, the only medical-domain-specific model, scored significantly lower on both dimensions (all Conclusions: In this evaluation, the four general-purpose models outperformed the single medical-domain-specific model examined in factual accuracy and completeness. However, systemic deficits in empathetic communication and actionable guidance persist. Model selection should prioritize holistic profile distributions over single-dimension scores. Developers must integrate de-stigmatizing corpora and action-oriented alignment; clinicians should position AI as supplementary to professional care. Future evaluations should adopt person-centred, multi-turn, and real-user validated frameworks.