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LLM-driven collaborative framework for knowledge-enhanced cancer pain assessment and management.
Haixiao Liu1, Yue Hu1, Dongtao Li1
1Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
NPJ Digital Medicine
|January 19, 2026
Summary
OncoPainBot, a novel framework using large language models (LLMs), enhances cancer pain assessment and management by simulating clinical experts. It demonstrates high accuracy in analgesic recommendations, improving personalized patient care.
Area of Science:
- Oncology
- Artificial Intelligence
- Clinical Decision Support
Background:
- Cancer pain presents complex challenges due to its multifaceted mechanisms, variable opioid responses, and adverse reactions.
- Current management strategies require improvement for comprehensive assessment and personalized treatment.
Purpose of the Study:
- To develop and evaluate OncoPainBot, a framework utilizing large language models (LLMs) for advanced cancer pain assessment and management.
- To identify the optimal LLM and Retrieval-Augmented Generation (RAG) configuration for clinical application.
Main Methods:
- Developed OncoPainBot, integrating four specialized agents: Pain-Extraction, Pain-Mechanism Reasoning, Treatment-Planning, and Safety-Check.
- Compared seven LLMs and three RAG strategies to determine the best model configuration.
- Validated the framework on 516 real-world electronic medical records of cancer pain.
Main Results:
- Claude-4 combined with RAG demonstrated superior performance, exhibiting strong semantic consistency and evidence-based reasoning.
- OncoPainBot achieved high consistency with clinical documents and a decision-making accuracy of 0.841 in analgesic recommendations.
- Error analysis indicated high reliability, with discrepancies mainly due to patient-specific factors, not incorrect drug selection.
Conclusions:
- OncoPainBot proves the feasibility of LLM-based systems for cancer pain management.
- The framework offers a transparent, evidence-based, and clinically relevant approach to personalized analgesic care.
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