{
  "abstract": "Objective: To compare how various Large Language Models (LLMs) perform when solving text- and image-based medical student-level radiology multiple-choice questions (MCQs). Methods: We evaluated the accuracy of seven LLMs (i.e., ChatGPT-4o, ChatGPT-5, ChatGPT-5 Plus, DeepSeek-V3.1-Terminus, Microsoft Copilot, Claude 4, and Gemini 2.5 Flash) in solving 60 text-based and 60 image-based MCQs. We compared the seven LLMs’ accuracy across question types, domains, and topics. Results: The mean accuracy across the seven evaluated LLMs was 79.9% (671/840 correct responses). Claude 4 achieved the highest observed accuracy at 86.7%, whereas Gemini 2.5 Flash and Microsoft Copilot recorded the lowest accuracy at 75.0% each. Accuracy was substantially lower for image-based questions than for text-based questions (64.5% vs. 95.2%). In the generalized estimating equations analysis, question type was significantly associated with response accuracy (Wald χ2(1) = 64.53, p < 0.001), as were question domain (Wald χ2(1) = 6.86, p = 0.009) and the question type × domain interaction (Wald χ2(3) = 80.26, p < 0.001). The omnibus effect of model was not statistically significant after accounting for these factors (Wald χ2(6) = 6.59, p = 0.361). Although Claude 4 achieved the highest observed accuracy, no pairwise model comparison remained statistically significant after Bonferroni correction. Conclusions: Large Language Models are less accurate at solving image-based than text-based radiology questions, supporting their role as adjunctive educational tools that require faculty oversight rather than independent replacements for radiology instruction.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Internal Medicine, Division of Radiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia",
    "Radiology Division, Department of Medicine, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Noha Bakhsh"
    },
    {
      "affs": [
        1
      ],
      "name": "Ghofran A. Ageely"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6s2qwjfjrh1j7.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360928",
  "fulltext_url": null,
  "id": "6s2qwjfjrh1j7",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0360928",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Learning and memory/Learning/Human learning"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Clinical oncology/Radiation therapy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Genitourinary imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical humanities/Medical education"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer treatment/Radiation therapy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Clinical oncology/Radiation therapy"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Genitourinary imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Interventional radiology"
    ],
    [
      "plos",
      "/People and places/Population groupings/Educational status/Undergraduates"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Genitourinary imaging"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Learning/Human learning"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Education/Medical education"
    ]
  ],
  "openalex": "W7225401011",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/6s2qwjfjrh1j7.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:20:24Z",
      "native_id": "10.1371/journal.pone.0360928",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360928"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
    }
  ],
  "title": "Enhancing medical education with AI: A comparative study of various large language models in radiology",
  "title_text": "Enhancing medical education with AI: A comparative study of various large language models in radiology",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:20:48Z",
  "url": "https://research.draconitic.com/paper/6s2qwjfjrh1j7",
  "venue": "PLOS One",
  "versions": []
}