LLM-driven collaborative framework for knowledge-enhanced cancer pain assessment and management.

Due to its multi-factor mechanism, variable opioid response, and high-risk adverse reactions, cancer pain remains a major challenge in oncology. To overcome these obstacles, we have developed a collaboration framework based on large language models (LLMs): OncoPainBot. This framework can simulate the reasoning and decision-making of multiple clinical experts to conduct comprehensive cancer pain assessment and management. Our OncoPainBot integrates four specialized agents: Pain-Extraction, Pain-Mechanism Reasoning, Treatment-Planning, and Safety-Check, each corresponding to a unique clinical role. In this paper, we compare seven LLMs and three Retrieval-Augmented Generation(RAG) strategies to determine the optimal model configuration. The final framework was verified on 516 real-world electronic medical records of cancer pain collected. We tested our solution through multiple dimensions. Ultimately, Claude-4 combined with RAG achieved the best overall performance, demonstrating outstanding semantic consistency and evidence-based reasoning in multiple metrics. In clinical validation, OncoPainBot achieved a high degree of consistency between the generated reports and actual clinical documents, while maintaining a high decision-making accuracy (0.841) in the analgesic recommendation task. At the same time, our error analysis shows that most of the differences are caused by patient-specific factors and monitoring recommendations rather than incorrect drug selection, which demonstrates the reliability of our framework. OncoPainBot has demonstrated the feasibility of a cancer pain management system based on LLMs, providing a transparent, evidence-based, and clinical-based framework for personalized analgesic care.