To develop a collaborative framework using large language models (LLMs) for enhanced assessment and management of cancer pain, addressing the significant challenges in oncology.
Key Findings:
Claude-4 combined with RAG provided the best performance in semantic consistency and evidence-based reasoning, crucial for clinical decision-making.
OncoPainBot achieved a decision-making accuracy of 0.841 in analgesic recommendations, indicating high reliability.
Differences in generated reports were primarily due to patient-specific factors rather than incorrect drug selection, underscoring the framework's adaptability.
Interpretation:
OncoPainBot demonstrates the potential of LLMs in providing a reliable, evidence-based framework for personalized cancer pain management, with implications for improving patient outcomes.
Limitations:
Data supporting findings are not publicly available due to privacy restrictions, which may limit external validation.
The framework's effectiveness may vary based on individual patient factors, suggesting a need for tailored applications.
Conclusion:
OncoPainBot shows feasibility as a cancer pain management system, offering a transparent and clinical-based approach to analgesic care, with potential for future research and application in diverse clinical settings.
