Introduction: Multidisciplinary tumor boards (MTBs) are the standard for oncologic decision-making but require substantial time and personnel resources. Large language models (LLMs) may support MTBs by generating structured, guideline-based recommendations. We evaluated concordance between MTB decisions and LLM recommendations in newly diagnosed cholangiocellular adenocarcinoma (CCA) and hepatocellular carcinoma (HCC). Method: In this retrospective single-center study, MTB protocols from 2022–2023 were screened. After exclusions, 50 CCA and 50 HCC cases were analyzed. Institutional MTB recommendations were compared with outputs from ChatGPT and Claude using identical structured clinical summaries available at the time of MTB presentation. Agreement was assessed using Cohen’s kappa, and correlation using Spearman’s rank coefficient. Results: For CCA, exact concordance between MTB and ChatGPT was 80%, with substantial agreement (k = 0.688) and strong correlation (r = 0.725; both p < 0.001). Concordance with Claude was at 56%. For HCC, concordance between MTB and ChatGPT was 66%, with substantial agreement (k = 0.604) and moderate correlation (r = 0.484; both p < 0.001). Concordance with Claude was only 38%, with fair agreement (k = 0.314, p < 0.001) and weak correlation (r = 0.086, p = 0.551). Conclusions: LLM-derived recommendations varied markedly by model. ChatGPT demonstrated substantial concordance, whereas Claude showed limited agreement. MTBs appeared more individualized, while LLM outputs were more guideline-oriented. These findings highlight the potential role of LLMs as supportive tools for structured clinical reasoning and guideline adherence. Prospective multicenter studies should evaluate real-time LLM integration into MTB workflows, efficiency, decision quality, and patient outcomes safely.

Surgical resection remains a cornerstone in the treatment of various hepatobiliary diseases. However, preoperative quality of life (QoL) in patients undergoing liver resection remains insufficiently studied. This study aimed to evaluate preoperative QoL and identify factors associated with impaired QoL in patients scheduled for liver surgery. This single-center prospective observational study included patients undergoing liver resection for hepatobiliary diseases. Preoperative QoL was assessed using the Short Form-36 (SF-36) Health Survey, comprising eight domains and the Physical and Mental Component Summary (PCS and MCS) scores. Scores were compared with reference values from the 1998 German Federal Health Survey. Univariate and multivariable linear regression analyses were performed to identify preoperative predictors of QoL. 159 patients were included in the final analysis. Of these, 20 (12.6%) had benign lesions, 80 (50.3%) primary malignant tumors, and 59 (37.1%) secondary malignant tumors. Compared with the general German population, patients demonstrated significantly lower scores in 7 of 8 SF-36 domains as well as in both summary scores. Multivariable analyses identified female sex, absence of preoperative chemotherapy, anemia, history of depression, cardiovascular disease, and renal disease as independent predictors of impaired QoL across multiple domains. The mean R² across all multivariable models was 0.23. Patients awaiting liver resection exhibit substantially impaired QoL compared with the general population. A combination of demographic, clinical, and laboratory factors influences preoperative QoL. Recognition of these factors may facilitate risk stratification, improve preoperative counseling, and support targeted interventions for patients at increased risk of impaired QoL.