Version 1
: Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (14:59:56 CEST)
How to cite:
Borkowski, A.; Ben-Ari, A. Muli-Agent AI Systems in Healthcare: Technical and Clinical Analysis. Preprints2024, 2024100182. https://doi.org/10.20944/preprints202410.0182.v1
Borkowski, A.; Ben-Ari, A. Muli-Agent AI Systems in Healthcare: Technical and Clinical Analysis. Preprints 2024, 2024100182. https://doi.org/10.20944/preprints202410.0182.v1
Borkowski, A.; Ben-Ari, A. Muli-Agent AI Systems in Healthcare: Technical and Clinical Analysis. Preprints2024, 2024100182. https://doi.org/10.20944/preprints202410.0182.v1
APA Style
Borkowski, A., & Ben-Ari, A. (2024). Muli-Agent AI Systems in Healthcare: Technical and Clinical Analysis. Preprints. https://doi.org/10.20944/preprints202410.0182.v1
Chicago/Turabian Style
Borkowski, A. and Alon Ben-Ari. 2024 "Muli-Agent AI Systems in Healthcare: Technical and Clinical Analysis" Preprints. https://doi.org/10.20944/preprints202410.0182.v1
Abstract
Scaling the operation is the single greatest challenge in every enterprise. The more complex the enterprise, the greater the challenge. Scaling in healthcare has been a consistent challenge, with the need to meet clinical endpoints with less staff, increased costs of resources, regulatory oversight, and the goal of improving access to care. The article explores the emerging paradigm of multi-agent AI systems in healthcare, representing a significant leap beyond traditional Large Language Models. We comprehensively analyze the potential of these systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. The paper describes a hypothetical sepsis management system comprising seven specialized AI agents, each handling specific aspects of patient care, from data collection and diagnosis to treatment recommendations and resource management. We also examine applications in chronic disease management and hospital patient flow optimization. The technical implementation of these systems is discussed, including the use of advanced Large Language Models, inter-agent quality control measures, implementing guardrails, self-reflection, integration with Electronic Health Records, and the importance of explainable AI for decision transparency. In addition to describing the promising potential benefits, such as enhanced diagnostic accuracy and personalized treatment plans, we address significant challenges, including data quality assurance, workflow integration, and ethical considerations. We conclude the article by highlighting future directions, such as integrating IoT devices and developing more sophisticated natural language interfaces. Our work underscores the transformative potential of multi-agent AI systems in healthcare while emphasizing the need for rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.
Keywords
AI-Agent; Multi AI Agents; Large Language Models, Healthcare; Clinical Operation; Agentic Systems
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
