PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
arXiv:2603.17356v2 Announce Type: replace Abstract: Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RA
The proliferation of broad medical knowledge in LLMs, coupled with their current limitations in nuanced patient-specific recommendations, creates an immediate need for advanced RAG techniques.
Improving AI's ability to provide accurate and personalized drug recommendations has significant implications for healthcare efficiency, patient outcomes, and the broader application of AI in sensitive domains.
The development of patient-aware, contextually rich RAG models specifically designed for clinical drug recommendations represents a notable improvement over generic guideline-based or similar-patient retrieval methods.
- · AI healthcare solution providers
- · Pharmaceutical companies leveraging AI for personalized medicine
- · Patients with complex conditions
- · Clinical decision support systems
- · Generic, one-size-fits-all medical AI models
- · Traditional drug recommendation algorithms
Improved drug recommendation accuracy for complex conditions.
Accelerated adoption of AI in clinical settings as trust and efficacy increase.
Shift towards highly personalized, AI-driven treatment plans across various medical specialties.
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Read at arXiv cs.CL