The DOI matches its Crossref record.
Multi-Agent System based Medical Diagnosis Using Particle Swarm Optimization in Healthcare
Abstract
Healthcare is an area that is challenging, dynamic, and diverse. Healthcare in the twenty-first century brings a variety of difficulties (population growth and rising healthcare costs). Compared to the conventional medical system, agent technology can offer superior healthcare for that. Agents in the hospital might deal with a variety of medical issues. We list collaboration across hospital wards, the development of diagnostics, the gathering of patient data, etc. as examples of issues that arise in hospitals. These issues can be resolved with a cooperative This paper proposes a generic architecture that integrates swarm intelligence with multi-agent healthcare systems to provide therapy as efficiently as possible.
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“By leveraging multimodal technologies, AI agents can simultaneously analyze various data sources—such as medical imaging, electronic health records, and genetic data—to assist in diagnosis, predict patient outcomes, and recommend personalized treatment plans.46,47,48”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [47]
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Research works printed but not linked: 11
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- Iantovics, 2007, The CMDS Medical Diagnosis System. Symbolic and Numeric Algorithms for Scientific Computing, IEEE Computer Society Press, 246
- 10.1007/978-3-0348-7976-7_6 · DOI
- 10.1080/08839519508945482 · DOI
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- Colorni, Distributed optimization by ant colonies, 134
- Wang, 2005, A Survey of Swarm Intelligence, Computer Engineering, 31, 194, 10.1063/1.2062963 · DOI
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- 2010, IBM and Cerner: Integrated healthcare management solutions, IBM New Orchard Road Armonk, NY 10504U.S.A. Produced in the United States of America March
- 2012, High-performance clinical information solutions: Delivering Epic’s market-leading healthcare software on a robust, scalable world-class infrastructure, IBM Route 100 Somers, NY 10589 U.S.A. Produced in the United States of America February
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A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41
Metadata
- Authors
- Dhatterwal, J. S., Singh Naruka, M., & Kaswan, K. S.
- Affiliations
- Koneru Lakshmaiah Education Foundation (IN)
- G L Bajaj Institute of Technology and Management,Greater Noida,India
- Galgotias University (IN)
- Venue
- 2023 International Conference on Artificial Intelligence and Smart Communication (AISC)
- Publisher
- IEEE
- Published
- 2023-01-27
- Pages
- 889-893
- OpenAlex
- W4362496529
- Cited by
- 27
- References
- 0
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 15
- FWCI
- 7.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Artificial Intelligence and Smart Communication (AISC) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- multi-agent systems, diagnosis, particle swarm optimization, swarm intelligence, health care system, patient data collection, healthcare cost reduction
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