Agent-based systems in healthcare
Open accessAbstract
This Special Issue is dedicated to discussing which are the advantages, challenges and open issues in the application of the agent-based approach as a part of the digital transformation in the healthcare sector. Agent-based technology in healthcare optimises resource allocation and coordination and supports clinical decision-making. Challenges, such as model reliability and interdisciplinary collaboration, must be addressed for widespread adoption. Embracing this technology promises improved healthcare delivery and better patient outcomes. Six papers, out of the many submitted, have been accepted for publication, each one discussing an aspect of this broad field.
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Agentic AI is characterized by multi-step, multi-layered tasks over extended time frames.
“Multi-step, multi-layered tasks over extended time frames”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Agentic AI enables real-time seizure prediction.
“real-time seizure prediction”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Agentic AI enables pinpoint risk stratification.
“Agentic AI enables pinpoint risk stratification”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Agentic AI enables robust human-robot interaction.
“robust human-robot interaction [59]”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Agentic AI enables rapid segmentation of medical images.
“rapid segmentation of medical images”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Agentic AI enables personalized diets from scratch.
“personalized diets from scratch”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [59]
Cites
Conversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · see citing works
Agent-Based Modeling in Public Health: Current Applications and Future Directions
Tracy, M., Cerdá, M., & Keyes, K. M. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014317 · Cited by 380 · see citing works
A survey on agents applications in healthcare: Opportunities, challenges and trends
Sulis, E., Mariani, S., & Montagna, S. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107525 · Cited by 25 · see citing works
Self-citationMulti-agent medical image segmentation: A survey
Bennai, M. T., Guessoum, Z., Mazouzi, S., Cormier, S., & Mezghiche, M. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107444 · Cited by 23 · see citing works
Agent-based dynamic optimization for managing the workflow of the patient's pathway
Ajmi, F., Zgaya, H., Othman, S. B., & Hammadi, S. (2019) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2019.101935 · Cited by 22 · see citing works
Breast cancer survival analysis agents for clinical decision support
Manzo, G., Pannatier, Y., Duflot, P., Kolh, P. H., Chavez, M., Bleret, V., Calvaresi, D., Jiménez–del–Toro, O. A., Schumacher, M., & Calbimonte, J.-P. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107373 · Cited by 21 · see citing works
Self-citationBertoncelli, C. M., Costantini, S., Persia, F., Bertoncelli, D., & D’Auria, D. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107548 · Cited by 21 · see citing works
Symbolic knowledge extraction for explainable nutritional recommenders
Magnini, M., Ciatto, G., Cantürk, F., Aydoğan, R., & Omicini, A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107536 · Cited by 11 · see citing works
Forming We-intentions under breakdown situations in human-robot interactions
Guerrero, E., Tewari, M., Kalmi, P., & Lindgren, H. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107817 · Cited by 4 · see citing works
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Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207 · see citing works
Worku, T., Gebeyehu, S., & Lakew, A. (2025) · The Electronic Journal of Information Systems in Developing Countries · doi:10.1002/isd2.70030
Perspectives on Decision Correctness in Distributed Systems: a Free Will Approach
Ciocan, A., & Pop, F. (2025) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs66924.2025.00107
- Authors
- Montagna, S., Mariani, S., Schumacher, M. I., & Manzo, G.
- Affiliations
- University of Urbino (IT)
- University of Modena and Reggio Emilia (IT)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- National Institutes of Health (US)
- Corresponding author
- Montagna, Sara
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 248
- Pages
- 108140
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W4392948947
- Cited by
- 3
- References
- 9
- Citations by year
- 2025: 3
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- CINECA IRIS Institutional Research information system (University of Urbino) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- agent-based systems, health care, digital transformation, resource allocation, clinical decision support, model reliability, interdisciplinary collaboration, health care delivery
- MeSH terms
- Clinical Decision-Making, Delivery of Health Care, Humans, Reproducibility of Results, Resource Allocation
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