# Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance

arXiv: https://arxiv.org/abs/2504.03699 · HTML: https://mlsanity.com/paper/2504.03699/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning](https://mlsanity.com/paper/2406.09187/) (2024)
- [A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://mlsanity.com/paper/2502.11211/) (2025)
- [MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents](https://mlsanity.com/paper/2501.14654/) (2025)
- [Datasheets for Healthcare AI: A Framework for Transparency and Bias Mitigation](https://mlsanity.com/paper/2501.05617/) (2025)
- [Psycho Gundam: Electroencephalography based real-time robotic control system with deep learning](https://mlsanity.com/paper/2411.06414/) (2024)
- [AI Managed Emergency Documentation with a Pretrained Model](https://mlsanity.com/paper/2408.09193/) (2024)

## Built on by (most cited first, 2 of 2)

- [CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support](https://mlsanity.com/paper/2601.03475/) (2026)
- [FRAC-MAS: A Safe and Explainable Multi-Agent System for Fracture Diagnosis](https://mlsanity.com/paper/2608.28662/) (2026)

## Abstract (arXiv)

Recent advances in the data-driven medicine approach, which integrates ethically managed and explainable artificial intelligence into clinical decision support systems (CDSS), are critical to ensure reliable and effective patient care. This paper focuses on comparing novel agent system designs that use modular agents to analyze laboratory results, vital signs, and clinical context, and to predict and validate results. We implement our agent system with the eICU database, including running lab analysis, vitals-only interpreters, and contextual reasoners agents first, then sharing the memory into the integration agent, prediction agent, transparency agent, and a validation agent. Our results suggest that the multi-agent system (MAS) performed better than the single-agent system (SAS) with mortality prediction accuracy (59%, 56%) and the mean error for length of stay (LOS)(4.37 days, 5.82 days), respectively. However, the transparency score for the SAS (86.21) is slightly better than the transparency score for MAS (85.5). Finally, this study suggests that our agent-based framework not only improves process transparency and prediction accuracy but also strengthens trustworthy AI-assisted decision support in an intensive care setting.