Collaborative LLM-Based Framework Enhances Cancer Pain Assessment and Management

Overview

OncoPainBot, a collaborative framework integrating large language models (LLMs), demonstrated high accuracy and consistency in cancer pain assessment and analgesic recommendation. The system, validated on 516 real-world records, achieved a decision-making accuracy of 0.841 and outperformed multiple LLM and retrieval strategies, highlighting its potential for personalized cancer pain management.

Background

Cancer pain is a complex clinical challenge due to multifactorial mechanisms, variable opioid responses, and significant adverse effects. Traditional pain management approaches often struggle with personalized treatment planning and safety monitoring. Advances in artificial intelligence, particularly large language models, offer opportunities to simulate expert clinical reasoning and improve decision-making. OncoPainBot was developed to leverage these technologies by integrating specialized agents to comprehensively assess and manage cancer pain.

Data Highlights

Key Findings

• OncoPainBot integrates four specialized LLM agents: Pain-Extraction, Pain-Mechanism Reasoning, Treatment-Planning, and Safety-Check.
• Among seven LLMs and three RAG strategies tested, Claude-4 combined with RAG achieved the best semantic consistency and evidence-based reasoning.
• The framework showed a high decision-making accuracy of 0.841 in analgesic recommendations validated against clinical records.
• Error analysis indicated discrepancies were mainly due to patient-specific factors and monitoring recommendations rather than incorrect drug selection.
• The system demonstrated high consistency between generated reports and actual clinical documentation, supporting its reliability.

Clinical Implications

OncoPainBot offers a transparent, evidence-based tool to support personalized cancer pain management by simulating expert clinical reasoning. Its high accuracy and consistency suggest it can assist clinicians in optimizing analgesic selection and safety monitoring. Integration of such AI-driven frameworks may enhance clinical workflows and improve patient outcomes in oncology pain care.

Conclusion

The OncoPainBot framework demonstrates the feasibility and reliability of using large language models for comprehensive cancer pain assessment and management, providing a promising approach for personalized analgesic care in clinical oncology.

References

1. Evenepoel et al. 2022 -- Pain Prevalence During Cancer Treatment: A Systematic Review and Meta-Analysis
2. Mulvey et al. 2024 -- Neuropathic pain in cancer: what are the current Guidelines?
3. Fallon et al. 2022 -- An international trial comparing analgesic ladder approaches in cancer
4. World Health Organization 1996 -- Cancer pain relief: with a guide to opioid availability
5. Abdel Shaheed et al. 2024 -- Opioid analgesics for nociceptive cancer pain: A comprehensive review
6. Van Veen et al. 2024 -- Adapted large language models can outperform medical experts in clinical text summarization