Work

AI-Generated Treatment Recommendations in Newly Diagnosed Lung Cancer: A Single-Center Feasibility Study in a Resource-Limited Setting

Year: 2026

Type: article

Abstract: BACKGROUND AI use in clinical decision making is increasingly frequent. How treatment recommendations generated by large language models (LLMs) compare with real-world treatment decisions remains poorly characterized. OBJECTIVE To assess whether AI-generated treatment recommendations are rated as appropriate as real-world MDT decisions in newly diagnosed lung cancer and to test the feasibility of a blinded comparative method. METHODS We designed a pilot study consisting of a blinded comparison of treatment recommendations made by four LLMs (ChatGPT, Claude, Gemini and Grok, accessed via consumer chat interfaces in June 2026) against real-world treatment decisions in a series of 27 consecutive, newly diagnosed lung cancer cases that received treatment in a single institution, from June 2024 to May 2026. A standardized prompt was used to ask AI to generate treatment recommendations. These were extracted in a standardized form, along with the actual treatment received in each case, totaling 135 treatment recommendations; a single blinded reviewer, presented with the recommendations in randomized order per case, graded all of them from 1 (inappropriate) to 5 (fully appropriate) and checked three flags (guideline-concordant, potentially unsafe, local/supportive treatment omitted). Scores were compared across sources using the Friedman rank-sum test. RESULTS The average score was highest for the real-world MDT decision at 4.52, followed by ChatGPT 4.48, Claude 4.44, Grok 4.41 and Gemini 4.37. No statistically significant difference was found (Friedman χ²(4)=1.99, p=0.74). No recommendation received a score lower than 3. Flags were similar across groups. CONCLUSIONS This pilot study did not detect a significant difference in rated appropriateness of treatment recommendations made by AI compared with the treatment actually received by patients. The methodology proved feasible but these findings are hypothesis-generating and require confirmation in larger studies, with more cases and multiple reviewers.

Source: Current Oncology

DOI: 10.3390/curroncol33100640

Authors: Andrei Popescu, Mara Jidveian-Popescu, Beatrice Mahler, Rodica Olteanu, Emanuel Clem

Institutions: Titu Maiorescu University, Spitalul Clinic Colentina, Carol Davila University of Medicine and Pharmacy, Institutul de Pneumoftiziologie "Marius Nasta"

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,654