A Smart Multimodal Healthcare Copilot with Powerful LLM Reasoning
This addresses misdiagnosis risks for healthcare systems and patients, but it appears incremental as it builds on retrieval-augmented generation and knowledge graphs.
The paper tackles the problem of misdiagnosis in healthcare by introducing MedRAG, a multimodal copilot that enhances medical decision-making through LLM reasoning, and it outperforms existing models on public and private datasets.
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.