mlsanityData to 13 Oct 2026

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care

arXiv 2503.16433 ↗First posted 9 Feb 2025cs.HC cs.CL cs.MAcited by 0

Not on the map. Why?

Abstract (arXiv)

Under-resourced or rural hospitals have limited access to medical specialists and healthcare professionals, which can negatively impact patient outcomes in sepsis. To address this gap, we developed the MATEC (Multi-AI Agent Team Care) framework, which integrates a team of specialized AI agents for sepsis care. The sepsis AI agent team includes five doctor agents, four health professional agents, and a risk prediction model agent, with an additional 33 doctor agents available for consultations. Ten attending physicians at a teaching hospital evaluated this framework, spending approximately 40 minutes on the web-based MATEC application and participating in the 5-point Likert scale survey (rated from 1-unfavorable to 5-favorable). The physicians found the MATEC framework very useful (Median=4, P=0.01), and very accurate (Median=4, P<0.01). This pilot study demonstrates that a Multi-AI Agent Team Care framework (MATEC) can potentially be useful in assisting medical professionals, particularly in under-resourced hospital settings.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 10 of its 35 references that are in the map. First posted on arXiv 9 Feb 2025.

Builds on

10 of 35 references.

References · 10

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,072
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,502
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,945
  5. 2023Towards Generalist Biomedical AIcited by 288
  6. 2024AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulatorcited by 100
  7. 2023Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitationscited by 98
  8. 2023Large Language Models to Identify Social Determinants of Health in Electronic Health Recordscited by 55
  9. 2024Large Language Model Distilling Medication Recommendation Modelcited by 25
  10. 2023Using large language models to promote health equitycited by 6

Built on by

Not cited yet.