Summary of Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models, By Akhil Vaid et al.

Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models

by Akhil Vaid, Joshua Lampert, Juhee Lee, Ashwin Sawant, Donald Apakama, Ankit Sakhuja, Ali Soroush, Sarah Bick, Ethan Abbott, Hernando Gomez, Michael Hadley, Denise Lee, Isotta Landi, Son Q Duong, Nicole Bussola, Ismail Nabeel, Silke Muehlstedt, Silke Muehlstedt, Robert Freeman, Patricia Kovatch, Brendan Carr, Fei Wang, Benjamin Glicksberg, Edgar Argulian, Stamatios Lerakis, Rohan Khera, David L. Reich, Monica Kraft, Alexander Charney, Girish Nadkarni

First submitted to arxiv on: 5 Jan 2024

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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 study evaluates the potential of Generative Large Language Models (LLMs) as autonomous agents in a simulated tertiary care medical center. Researchers assessed proprietary and open-source LLMs, with Retrieval Augmented Generation (RAG) enhancing contextual relevance. The results show that proprietary models, particularly GPT-4, outperformed open-source models in terms of guideline adherence and response accuracy. Expert clinicians’ manual evaluation was crucial in validating model outputs, emphasizing the importance of human oversight. The study highlights Natural Language Programming (NLP) as a key paradigm for modifying model behavior, allowing for precise adjustments through tailored prompts and real-world interactions.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large language models have big potential in healthcare! These AI-powered tools can help doctors and nurses make decisions by providing important information. But right now, they’re not perfect – sometimes the answers are old or wrong. This study looked at how well these models do when working alone in a pretend hospital setting. It found that some proprietary models did better than others, especially with special tricks to help them understand the questions. Experts reviewed what the models said and made sure it was good enough. The study also shows how we can make these models work better by giving them instructions and letting them learn from real-world experiences.