mlsanityData to 7 Oct 2026

Rx Strategist: Prescription Verification using LLM Agents System

arXiv 2409.03440 ↗First posted 5 Sep 2024cs.CLcited by 7

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

To protect patient safety, modern pharmaceutical complexity demands strict prescription verification. We offer a new approach - Rx Strategist - that makes use of knowledge graphs and different search strategies to enhance the power of Large Language Models (LLMs) inside an agentic framework. This multifaceted technique allows for a multi-stage LLM pipeline and reliable information retrieval from a custom-built active ingredient database. Different facets of prescription verification, such as indication, dose, and possible drug interactions, are covered in each stage of the pipeline. We alleviate the drawbacks of monolithic LLM techniques by spreading reasoning over these stages, improving correctness and reliability while reducing memory demands. Our findings demonstrate that Rx Strategist surpasses many current LLMs, achieving performance comparable to that of a highly experienced clinical pharmacist. In the complicated world of modern medications, this combination of LLMs with organized knowledge and sophisticated search methods presents a viable avenue for reducing prescription errors and enhancing patient outcomes.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 15 of its 16 references that are in the map. First posted on arXiv 5 Sep 2024.

Builds on

15 of 16 references.

References · 15

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2024Qwen2 Technical Reportcited by 2,076
  6. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  7. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  8. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  9. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  10. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  11. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  12. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 157
3 more
  1. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
  2. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 55
  3. 2024Measuring and Reducing LLM Hallucination without Gold-Standard Answerscited by 31

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Human-Level and Beyond: Benchmarking Large Language Models Against Clinical Pharmacists in Prescription Review
  2. 2024MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
  3. 2026A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
  4. 2026SafeRx-Agent: A Knowledge-Grounded Multi-Agent Framework for Safe and Explainable Medication Recommendation
  5. 2025LLM Agent Swarm for Hypothesis-Driven Drug Discovery
  6. 2025DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery
  7. 2026Designing a Robust LLM-Based Evaluation System for Agentic AI in Drug Discovery Through Human Alignment
  8. 2025PharmAgents: Building a Virtual Pharma with Large Language Model Agents