Authors: Himanshu Pandey, Akhil Amod, Shivang

Published on: April 27, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2404.17977

Summary

• What is new: Utilizes Swarm-Structured Multi-Agent Systems for reviewing patient-specific data against clinical guidelines, with a focus on the impact of prompting strategies on AI agents and benchmarking Large Language Models for task accuracy.
• Why this is important: The complexity of establishing medical necessity by reviewing vast amounts of patient-specific data.
• What the research proposes: Decomposing the task into sub-tasks handled by specialized AI agents that use various prompting strategies and Large Language Models.
• Results: Defined benchmarking standards for LLM accuracy and demonstrated how specialized agents can provide explainability to enhance system transparency.

Technical Details

Technological frameworks used: Swarm-Structured Multi-Agent Systems

Models used: Large Language Models

Data used: Patient-specific structured and unstructured medical data

Potential Impact

Healthcare providers, medical insurance companies, health-tech startups focusing on AI and machine learning solutions for clinical decision support

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