{
  "abstract": "This study aimed to evaluate the accuracy of 2 large language models (LLMs) – ChatGPT-5.0 and Gemini Flash 2.5 – in assessing refractive-surgery eligibility and recommending appropriate procedures, using refractive-surgeon consensus as the reference standard. This retrospective, single-center study included the right eyes of 40 consecutive patients evaluated for refractive surgery at Başkent University Hospital (Istanbul, Türkiye). To avoid inter-eye correlation, only the right eye of each patient was included. Standardised refractive and corneal tomographic data obtained using the Pentacam HR were independently reviewed by 2 refractive surgeons and by the 2 LLMs. Each model received identical English-language prompts once per eye. Outputs were classified as “eligible,” “borderline,” or “ineligible”; for all binary statistical analyses, borderline assessments were grouped with the ineligible category. Recommended surgical procedures were recorded separately, with more than 2 procedure permitted for the same eye. Agreement between experts and between each LLM and expert consensus was assessed using Gwet’s agreement coefficient 1 (AC1) coefficient. Inter-expert agreement was good (AC1 = 0.78). Compared with expert consensus, ChatGPT-5.0 demonstrated very good agreement (AC1 = 0.92), outperforming Gemini Flash 2.5, which showed good agreement (AC1 = 0.78). Both LLMs accurately identified most “eligible” eyes; however, specificity and negative predictive values exhibited wide confidence intervals due to the limited number of “ineligible” cases. ChatGPT-5.0 showed higher sensitivity (92.1%) and negative predictive value (40.0%) than Gemini Flash 2.5 (81.6% and 22.2%, respectively). Notably, neither model recommended implantable collamer lens surgery, even when clinically appropriate. ChatGPT-5.0 demonstrated very good agreement with expert consensus and higher agreement than Gemini Flash 2.5 in refractive-surgery eligibility assessment. Although both LLMs show potential as adjunctive decision-support tools, the omission of large language model recommendations and prompt-dependent variability limit their suitability for autonomous clinical use. Further optimization using comprehensive refractive-surgery datasets is required to improve clinical reliability.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Ophthalmology, Başkent University, Istanbul, Türkiye",
    "Department of Ophthalmology, Fırat University, Elazığ, Türkiye."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "İlter İritaş",
      "orcid": "0000-0003-1789-4787"
    },
    {
      "affs": [
        1
      ],
      "name": "Murat Erdağ"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/27ev4rv563xff.bib",
  "cited_by": 0,
  "doi": "10.1097/md.0000000000051367",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13727163/?tool=EBI",
  "id": "27ev4rv563xff",
  "issns": [
    "0304-5412",
    "1536-5964",
    "0025-7974"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13727163",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "5800"
    ],
    [
      "heading",
      "Observational Study"
    ],
    [
      "heading",
      "Research Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13727163/pdf/?tool=EBI",
  "pmcid": "PMC13727163",
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Wolters Kluwer Health",
  "ris": "https://research.draconitic.com/paper/27ev4rv563xff.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:51Z",
      "native_id": "PMC:PMC13727163",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13727163"
    },
    {
      "first_seen": "2026-11-05T12:23:18Z",
      "native_id": "PMC13727163",
      "source": "pmc",
      "url": "https://doi.org/10.1097/md.0000000000051367"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Can artificial intelligence match refractive surgeons? : A comparative analysis of refractive surgery candidate selection",
  "title_text": "Can artificial intelligence match refractive surgeons? : A comparative analysis of refractive surgery candidate selection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:23:18Z",
  "url": "https://research.draconitic.com/paper/27ev4rv563xff",
  "venue": "Medicine",
  "versions": []
}