Current oncology
AI-Generated Treatment Recommendations in Newly Diagnosed Lung Cancer: A Single-Center Feasibility Study in a Resource-Limited Setting
Par Popescu A, Jidveian-Popescu M, Mahler B et 2 autres.
Contenu en anglais
Résumé
Background
How treatment recommendations generated by large language models (LLMs) compare with real-world decisions in oncology remains poorly characterized, particularly in resource-limited settings.
Methods
In 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.