Summary of Development and Testing Of a Novel Large Language Model-based Clinical Decision Support Systems For Medication Safety in 12 Clinical Specialties, by Jasmine Chiat Ling Ong et al.
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties
by Jasmine Chiat Ling Ong, Liyuan Jin, Kabilan Elangovan, Gilbert Yong San Lim, Daniel Yan Zheng Lim, Gerald Gui Ren Sng, Yuhe Ke, Joshua Yi Min Tung, Ryan Jian Zhong, Christopher Ming Yao Koh, Keane Zhi Hao Lee, Xiang Chen, Jack Kian Chng, Aung Than, Ken Junyang Goh, Daniel Shu Wei Ting
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for Clinical Decision Support Systems (CDSS) is proposed, which combines Retrieval Augmented Generation (RAG) with Large Language Models (LLMs). The RAG-Large LLM framework aims to improve the accuracy and safety of medication prescriptions by integrating natural language processing techniques. By leveraging the strengths of both retrieval-based and generation-based approaches, this framework can provide clinicians with more informed decision-making tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to help doctors make better choices about medicines. This method uses computers to analyze a lot of information about medications and medical conditions. It helps doctors get the best treatment for patients by giving them advice based on what they know. This can be very helpful in making sure people get the right medicine and don’t have any bad side effects. |
Keywords
» Artificial intelligence » Natural language processing » Rag » Retrieval augmented generation
