{
  "abstract": "Background\n\nChinese 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\n\nA 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\n\nThe 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 P < 0.001). Its responses were less consistent with current guidelines and included one instance of implicit stigmatizing language. All models showed systemic deficits in caring (median = 3.0/5) and actionability (max = 1.70/3). Understandability showed a ceiling effect and no significant profile-level differences, although small model-level differences were observed. LPA identified four profiles: High Accuracy and Completeness (50.0%), Moderate-Balanced (3.6%), Low-Quality (18.8%), and High Completeness and Actionability-Oriented (27.6%). Network analysis identified completeness as the most central node (strength = 0.813) and actionability as a key bridge (betweenness = 0.167). A weak negative partial correlation between accuracy and actionability (r = -0.083) suggested a potential trade-off between technical precision and practical guidance at the statistical level. Profile distributions differed by model (χ² = 124.99, P < 0.001) but not domain (P = 0.625), indicating heterogeneity stems from model-specific characteristics. Yuanbao-Hy3 showed the most balanced humanistic-technical profile. DeepSeek-V4 excelled technically but lacked caring.Conclusions\n\nIn 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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, Hunan, China.",
    "School of Medical Information Engineering, Jining Medical University, Rizhao, Shandong, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinhao Gu"
    },
    {
      "affs": [
        0
      ],
      "name": "Jianwei Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yunrong Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Pingping Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Dehua Hu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qizhen Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1z9665adsdr1y.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1966522",
  "fulltext_url": "https://europepmc.org/articles/PMC13680460",
  "id": "1z9665adsdr1y",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903209",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220664661",
  "pdf_url": "https://europepmc.org/articles/PMC13680460?pdf=render",
  "pmcid": "PMC13680460",
  "pmid": "42903209",
  "published": "2026-10-06",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/1z9665adsdr1y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903209",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903209"
    },
    {
      "first_seen": "2026-10-30T12:20:22Z",
      "native_id": "10.3389/fpubh.2026.1966522",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1966522/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Evaluating Chinese large language models for HIV/AIDS health information: a multidimensional comparative study of response quality.",
  "title_text": "Evaluating Chinese large language models for HIV/AIDS health information: a multidimensional comparative study of response quality.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:22Z",
  "url": "https://research.draconitic.com/paper/1z9665adsdr1y",
  "venue": "Frontiers in public health",
  "versions": []
}