Summary of Medinsight: a Multi-source Context Augmentation Framework For Generating Patient-centric Medical Responses Using Large Language Models, by Subash Neupane et al.
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models
by Subash Neupane, Shaswata Mitra, Sudip Mittal, Noorbakhsh Amiri Golilarz, Shahram Rahimi, Amin Amirlatifi
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces MedInsight, a novel framework that combines large language models with domain-specific knowledge to generate patient-centric responses in healthcare settings. The proposed framework retrieves relevant information from medical records, consultations, authoritative texts, and curated web resources to construct an enriched context. This augmented input is then used to generate comprehensive and contextual responses for applications such as diagnosis, treatment recommendations, or patient education. Experiments on the MTSamples dataset demonstrate MedInsight’s effectiveness in generating relevant medical responses, while quantitative evaluation using Ragas metric and TruLens confirms its efficacy. Human evaluation studies involving Subject Matter Experts further validate MedInsight’s utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new way to use computer models to help doctors and patients communicate better. Right now, these computer models can talk like humans, but they don’t know much about specific topics like medicine. To fix this, the authors created MedInsight, a system that combines medical information from different sources with what a patient says. This helps generate responses that are relevant and helpful for things like diagnosing illnesses or recommending treatments. The authors tested MedInsight on some sample data and showed it works well. They also asked medical experts to look at the results and they said it’s useful. |
