Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912597936177152 |
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| author | Chen, Ying-Jung Albarqawi, Ahmad Chen, Chi-Sheng |
| author_facet | Chen, Ying-Jung Albarqawi, Ahmad Chen, Chi-Sheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance Chen, Ying-Jung Albarqawi, Ahmad Chen, Chi-Sheng Artificial Intelligence Computers and Society Machine Learning Multiagent Systems Quantitative Methods 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. |
| title | Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance |
| topic | Artificial Intelligence Computers and Society Machine Learning Multiagent Systems Quantitative Methods |
| url | https://arxiv.org/abs/2504.03699 |