Beyond Accuracy: Multidimensional Evaluation of Large Language Models in Hepatocellular Carcinoma Management Emphasizing Prompting

Abstract Background 2.60 ± 0.06, 95%) and interpretability (0.43;0.43). Prompting significantly improved accuracy ( p < 0.001) and interpretability ( p < 0.001) across all models. Semantic consistency declined slightly in most models; information entropy generally increased; readability changes varied. Conclusions This study presents the first multidimensional evaluation of large language models in hepatocellular carcinoma–related clinical tasks. General-purpose models outperformed some medical models, revealing limitations in domain-specific fine-tuning. Prompt design strongly influenced model performance. Further research should integrate diverse prompt strategies and clinical scenarios to improve the usability of language models in real-world oncology settings. Lay summary This study evaluated how well-advanced language-based artificial intelligence models can answer clinical questions related to hepatocellular carcinoma. The results showed that some models, especially when guided with structured instructions, provided accurate and understandable responses. These findings suggest that such tools may help improve communication and access to information for both doctors and patients managing liver cancer.