mlsanityData to 7 Oct 2026

A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning

arXiv 2506.02470 ↗First posted 3 Jun 2025cs.AIcited by 3

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Abstract (arXiv)

Misdiagnosis causes significant harm to healthcare systems worldwide, leading to increased costs and patient risks. MedRAG is a smart multimodal healthcare copilot equipped with powerful large language model (LLM) reasoning, designed to enhance medical decision-making. It supports multiple input modalities, including non-intrusive voice monitoring, general medical queries, and electronic health records. MedRAG provides recommendations on diagnosis, treatment, medication, and follow-up questioning. Leveraging retrieval-augmented generation enhanced by knowledge graph-elicited reasoning, MedRAG retrieves and integrates critical diagnostic insights, reducing the risk of misdiagnosis. It has been evaluated on both public and private datasets, outperforming existing models and offering more specific and accurate healthcare assistance. A demonstration video of MedRAG is available at: https://www.youtube.com/watch?v=PNIBDMYRfDM. The source code is available at: https://github.com/SNOWTEAM2023/MedRAG.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

10 of 27 references.

References · 10

  1. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  3. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  4. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 89
  5. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilotcited by 83
  6. 2024Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultationcited by 17
  7. 2020Brain-inspired Search Engine Assistant based on Knowledge Graphcited by 13
  8. 2024A Generalizable Anomaly Detection Method in Dynamic Graphscited by 10
  9. 2024Almanac Copilot: Towards Autonomous Electronic Health Record Navigationcited by 9
  10. 2024A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosiscited by 4

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPOcited by 0
  2. 2025Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspectivecited by 0
  3. 2025GEM: Generative Entropy-Guided Preference Modeling for Few-shot Alignment of LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval
  2. 2026MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
  3. 2025Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning
  4. 2026MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis
  5. 2025MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis
  6. 2026MedTRACE: Tool-Augmented Multimodal Clinical Reasoning Agents for Evidence-Grounded Decision-Making
  7. 2025Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards
  8. 2025MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text