{
  "abstract": "Background\n\nHow treatment recommendations generated by large language models (LLMs) compare with real-world decisions in oncology remains poorly characterized, particularly in resource-limited settings.Methods\n\nIn this single-center feasibility study, four LLMs (ChatGPT, Claude, Gemini, and Grok, accessed via consumer interfaces in June 2026) generated treatment recommendations for 27 consecutive newly diagnosed lung cancer cases treated at a Romanian oncology unit (June 2024-May 2026). Each model's output and the treatment actually delivered were formatted identically (135 recommendations total), presented in randomized order, and scored for appropriateness (1-5) by a reviewer blinded to the source, with three quality flags. A second independent reviewer re-scored all recommendations to assess inter-rater reliability. Scores were compared using the Friedman test.Results\n\nMean appropriateness was highest for the real-world decision (4.52), followed by ChatGPT (4.48), Claude (4.44), Grok (4.41), and Gemini (4.37), with no significant difference (Friedman χ²₄ = 1.99, p = 0.74). No recommendation scored below 3. Inter-rater agreement was high in raw terms (93.3% within one point) but only fair after chance correction (weighted κ = 0.26), reflecting a ceiling effect.Conclusions\n\nAI-generated recommendations were rated similarly to real-world decisions. The evaluation method proved feasible in a resource-limited setting; the small sample and ceiling effect preclude equivalence claims, and the findings are hypothesis-generating.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Oncology, Colentina Clinical Hospital, 020125 Bucharest, Romania.",
    "Faculty of Medicine, Titu Maiorescu University, 031593 Bucharest, Romania.",
    "Department of Medical Oncology, CF2 Clinical Hospital, 011464 Bucharest, Romania.",
    "Department of Pneumology, \"Marius Nasta\" Institute of Pneumology, 050159 Bucharest, Romania.",
    "Department of Pulmonology, \"Carol Davila\" University of Medicine and Pharmacy, 020021 Bucharest, Romania.",
    "Department of Dermatology, Colentina Clinical Hospital, 020125 Bucharest, Romania."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Andrei Popescu"
    },
    {
      "affs": [
        2
      ],
      "name": "Mara Jidveian-Popescu"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Beatrice Mahler",
      "orcid": "0000-0002-9240-922X"
    },
    {
      "affs": [
        1,
        5
      ],
      "name": "Rodica Olteanu"
    },
    {
      "affs": [
        1
      ],
      "name": "Emanuel Clem"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0zq65h04zpwr6.bib",
  "cited_by": 0,
  "doi": "10.3390/curroncol33100640",
  "fulltext_url": "https://europepmc.org/articles/PMC13703460",
  "id": "0zq65h04zpwr6",
  "issns": [
    "1198-0052",
    "1718-7729"
  ],
  "landing_url": "https://europepmc.org/article/MED/42930207",
  "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": "W7223172250",
  "pdf_url": "https://europepmc.org/articles/PMC13703460?pdf=render",
  "pmcid": "PMC13703460",
  "pmid": "42930207",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0zq65h04zpwr6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42930207",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42930207"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "AI-Generated Treatment Recommendations in Newly Diagnosed Lung Cancer: A Single-Center Feasibility Study in a Resource-Limited Setting.",
  "title_text": "AI-Generated Treatment Recommendations in Newly Diagnosed Lung Cancer: A Single-Center Feasibility Study in a Resource-Limited Setting.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/0zq65h04zpwr6",
  "venue": "Current oncology (Toronto, Ont.)",
  "versions": []
}