Agentic systems in radiology: Principles, opportunities, privacy risks, regulation, and sustainability concerns.
Open accessAbstract
The rapid rise of transformer-based large language models (LLMs) has introduced new opportunities for automation and decision support in radiology, particularly in applications such as report generation, protocol optimization, and structured interpretation. Despite their impressive performance in producing contextually coherent text, conventional LLMs remain limited by their inability to interact autonomously with external systems, retrieve data, or execute code, restricting their role in real-world clinical and research workflows. To address these limitations, agentic systems have emerged as a new paradigm. By embedding LLMs within frameworks that enable reasoning, planning, and action, agentic systems extend LLM capabilities to dynamic interaction with users, tools, and data sources. This review provides a comprehensive overview of the foundations, architectures, and operational mechanisms of agentic systems, focusing on their applications in medical imaging and radiology. It summarizes key developments in the literature, including recent multi-agent frameworks for automated radiomics pipelines, and discusses the potential benefits of these systems in enhancing the reproducibility, interpretability, and accessibility of AI-driven workflows. The review critically examines current regulatory considerations, ethical implications, and sustainability challenges to highlight essential gaps that must be addressed for the safe and responsible clinical integration of these systems.
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The Chinese HCC imaging study [24] applied a six-agent multimodal RAG design where agents specialised in handling imaging features, textual reports, and reasoning over combined evidence.
“The Chinese HCC imaging [24]applied a six-agent multimodal RAG design, where agents specialised in handling imaging features, textual reports, and reasoning over combined e-ISSN: 2548-6861 JAIC Vol. 10, No. 1, February 2026: 171 -183 evidence.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
The multimodal HCC 6-agent system relied on high-performance GPUs and large annotated imaging datasets, limiting deployment beyond advanced radiology centres.
“The multimodal HCC 6-agent system [24], for example, relied on high-performance GPUs and large annotated imaging datasets, restrictions that limit deployment beyond advanced radiology centres.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
Multimodal systems add transparency by demonstrating how image features are connected to text retrieval to draw a conclusion.
“Multimodal [23][24] also added transparency by demonstrating how the image features were connected to text retrieval in order to draw a conclusion.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
Clinicians trust outputs more in multimodal domains because multimodal agent reasoning lets them see how imaging observations are combined with text-based evidence.
“Clinicians trust outputs more in multimodal domains [24] because multimodal agent reasoning lets clinicians see exactly how imaging observations are combined with text-based evidence informed predictions.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
Multimodal multi-agent models demonstrated superior diagnostic performance by integrating imaging and text more effectively than classical CNN-based or text-only systems.
“while multimodal multiagent models demonstrated superior diagnostic performance by integrating imaging and text more effectively than classical CNN-based or text-only systems [23][24].”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
Multimodal agent specialisation, where different agents handle imaging, textual data, and fusion reasoning, further enhances explainability.
“The explainability in [24] was further enhanced with the use of multimodal agent specialisation where different agents handled imaging, textual data, and fusion reasoning.”
From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [24]
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- Authors
- Tzanis, E., Adams, L. C., Akinci D’Antonoli, T., Bressem, K. K., Cuocolo, R., Kocak, B., Malamateniou, C., & Klontzas, M. E.
- Affiliations
- University of Crete (GR)
- TUM Klinikum (DE)
- University of Salerno (IT)
- City St George's, University of London (GB)
- Corresponding author
- Klontzas, Michail E.
- ORCID
- Venue
- Diagnostic and Interventional Imaging
- Publisher
- Elsevier BV
- Published
- 2026-01
- Volume
- 107
- Issue
- 1
- Pages
- 7-16
- ISSN
- 2211-5684 (ISSN-L)
- OpenAlex
- W4415534137
- Cited by
- 16
- References
- 52
- Citations by year
- 2026: 15 · 2025: 1
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diagnostic and Interventional Imaging (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- agentic systems, large language models, radiology, multi-agent systems, medical report generation, clinical decision support, privacy risks, AI regulation, AI ethics, AI sustainability, diagnostic imaging
- MeSH terms
- Artificial Intelligence, Humans, Radiology, Radiology Information Systems
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