SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation
arXiv:2605.29146v1 Announce Type: cross Abstract: Medication recommendation predicts medications for patient visits, but existing methods still face two key challenges. At the model level, traditional drug recommendation methods only predict structured drug codes with limited evidence grounding, while LLM agents can use richer clinical context but may lack safety verification and traceability. At the task level, existing benchmarks often use broad medication categories, which ignore subgroup-level safety differences and can lead to risk overestimation. We introduce the first fine-grained medic
The rapid advancement of large language models (LLMs) and their integration into agentic systems are pushing the boundaries of AI applications in sensitive domains like healthcare.
This development highlights the crucial need for safety verification, explainability, and fine-grained data in AI systems operating in high-stakes environments, directly addressing current limitations in medical AI.
Medication recommendation systems are moving beyond basic structured data to incorporate rich clinical context via LLM agents, alongside a new emphasis on fine-grained safety and explainability in their design.
- · AI healthcare providers
- · Patients
- · Healthcare data scientists
- · LLM developers focused on safety
- · Traditional drug recommendation methods
- · Companies relying on broad medication categories
- · AI developers ignoring safety and explainability
Improved patient outcomes due to more accurate and safer medication recommendations.
Increased trust in AI-driven healthcare solutions leading to wider adoption in clinical settings.
Potential for new regulatory frameworks and industry standards specifically for explainable and safe medical AI agents.
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Read at arXiv cs.AI