{
  "abstract": "Objective To compare four patient-facing AI assistants for chronic constipation education across expert-rated quality, objective readability, and patient-reported acceptability. Methods In this two-phase study, 15 common questions and two clinical scenarios were submitted once to ChatGPT-4o, DeepSeek V3, Qwen3, and MedGo V1 using the same standardized Chinese prompt. Six blinded experts rated 68 responses across six domains. Objective readability was assessed using Chinese character count, reading difficulty score, and recommended reading age. In Phase 2, 30 patients each posed three questions; the investigator entered each question into all four assistants in a fixed sequence, and patients rated eight acceptability domains. Friedman tests were used; significant expert and patient results underwent Holm-adjusted Wilcoxon signed-rank tests. Results Accuracy ( p = .175) and safety ( p = .068) did not differ significantly across models. Domain specificity, expert-rated readability, humanity, and actionability differed significantly. Qwen3 scored higher than the other models for domain specificity and actionability in adjusted comparisons. MedGo V1 produced the shortest responses and had the lowest mean reading difficulty and recommended reading age. Patient ratings showed a different pattern: DeepSeek V3 had the highest mean overall acceptability (4.22 ± 0.19), exceeding Qwen3 (adjusted p < .001) and MedGo V1 (adjusted p = .044), but not ChatGPT-4o (adjusted p = .053). Conclusion Expert-rated quality, text complexity, and short-term patient acceptability were not fully aligned. No single assistant consistently outperformed the others. These findings describe model-specific performance profiles but do not establish clinical effectiveness, implementation readiness, or superiority of general-purpose versus medical-specialized systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Yanfen Gu MSc"
    },
    {
      "name": "Shiqi Qiang MSc"
    },
    {
      "name": "Zehui Xu PhD"
    },
    {
      "name": "Haitao Zhang MSc"
    },
    {
      "name": "Xiaoxiao Peng BS"
    },
    {
      "name": "Haiping Yu PhD"
    },
    {
      "name": "Meidong Xu PhD"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dpefr53r638cd.bib",
  "cited_by": 0,
  "doi": "10.1177/20552076261497162",
  "fulltext_url": "https://doi.org/10.1177/20552076261497162",
  "id": "dpefr53r638cd",
  "issns": [
    "2055-2076"
  ],
  "landing_url": "https://doi.org/10.1177/20552076261497162",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223452287",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "SAGE Publishing",
  "ris": "https://research.draconitic.com/paper/dpefr53r638cd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:47Z",
      "native_id": "b34a1b6ab00e40c29a87572c7910759d",
      "source": "doaj",
      "url": "https://doi.org/10.1177/20552076261497162"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Patient-facing large language model assistants for chronic constipation education: A comparative study of expert evaluation, readability, and patient acceptability",
  "title_text": "Patient-facing large language model assistants for chronic constipation education: A comparative study of expert evaluation, readability, and patient acceptability",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:47Z",
  "url": "https://research.draconitic.com/paper/dpefr53r638cd",
  "venue": "Digital Health",
  "versions": []
}