Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance
Journal:
arXiv
Published Date:
Mar 25, 2025
Abstract
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.
