{
  "abstract": "We retrospectively compared Epic-generated draft replies with institutionally managed hybrid GPT-4.1 and Gemini 2.5 Pro responses to 51 selected single-turn radiation oncology messages. Six clinicians, blinded to response source, rated overall quality on a five-point scale and perceived editing effort on a four-point scale. Mixed-effects models accounted for repeated ratings by message and clinician. Adjusted overall-quality means were 2.95 for Epic, 4.27 for hybrid GPT-4.1, and 4.39 for hybrid Gemini 2.5 Pro. Corresponding editing-effort means were 2.38, 1.46, and 1.41, with lower scores indicating less perceived effort. Both hybrid models received more favorable ratings than Epic for both outcomes (all Holm-adjusted P < 0.001), without a statistically significant difference between the hybrid models. These findings support prospective evaluation of clinician-supervised draft generation, though editing time and patient outcomes were not measured, and clinical safety was not established.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.34464",
  "author_total": 14,
  "authors": [
    {
      "name": "Meiyun Cao"
    },
    {
      "name": "Federico Mastroleo"
    },
    {
      "name": "Shuoyang Wei"
    },
    {
      "name": "Yuexing Hao"
    },
    {
      "name": "Srinivas Seetamsetty"
    },
    {
      "name": "Keldon K. Lin"
    },
    {
      "name": "Madhurya Reddy"
    },
    {
      "name": "Yuzhen Ding"
    },
    {
      "name": "Jason Holmes"
    },
    {
      "name": "Jonathan E. Moonen"
    },
    {
      "name": "Shyanne L. Dobbs"
    },
    {
      "name": "Diego Santos Toesca"
    },
    {
      "name": "Mark Waddle"
    },
    {
      "name": "Wei Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/317mm0704qax3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.34464",
  "fulltext_url": null,
  "id": "317mm0704qax3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.34464",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7223914785",
  "pdf_url": "https://arxiv.org/pdf/2610.34464",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/317mm0704qax3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:35Z",
      "native_id": "2610.34464",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.34464"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Retrospective Comparison of Radiation Oncology Patient Message Responses Generated by Epic and Institutionally Managed LLMs",
  "title_text": "Retrospective Comparison of Radiation Oncology Patient Message Responses Generated by Epic and Institutionally Managed LLMs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:35Z",
  "url": "https://research.draconitic.com/paper/317mm0704qax3",
  "venue": "arXiv",
  "versions": []
}