Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study

Background: Large language models (LLMs) and large reasoning models (LRMs) have shown excellent performance on medical benchmarks, although evaluations concerning real-world medical workflows are still lacking. Lung cancer care is particularly dependent on the multidisciplinary team (MDT) integration of radiology, pathology, staging, and treatment planning, making it a high-bar setting for evaluating LRMs. Objective: This study aimed to compare the quality of recommendations generated by 2 LRMs (GPT-5-Thinking and Deepseek-v3-r1) with each other and with human MDT decisions in real-world lung cancer cases, as well as to assess whether MDT awareness of AI comparison influences the quality of MDT decisions. Methods: This was a single-center real-world comparative study of 100 consecutive lung cancer MDT cases (50 retrograde and 50 anterograde) from the University Hospital of Split, Croatia. For each case, deidentified structured reports (containing all necessary patient or case data, while excluding MDT conclusions) were submitted once to GPT-5-Thinking and Deepseek-v3-r1 to generate recommendations for radiologic diagnostics, pathologic diagnostics, oncologic therapy, and overall usefulness. Two independent lung oncologists graded MDT decisions and model outputs on 1-5 Likert scales. An average recommendation score (avg_rec) was calculated as the mean of radiology, pathology, and therapy scores. Analyses used Wilcoxon tests for paired model comparisons, Mann-Whitney tests for between-phase comparisons, and Spearman correlations (2-sided α=.05). Results: Ratings showed ceiling effects. In the retrograde phase (N=50), the mean (95% CI) GPT-5-Thinking scores were higher than Deepseek-v3-r1 scores for radiologic diagnostics (4.89, 4.78-4.99 vs 4.76, 4.62-4.89; P