Agentic systems in radiology: Principles, opportunities, privacy risks, regulation, and sustainability concerns

Comprehensive Summary

This is a narrative review that deals with the current applications, designs, and challenges associated with the use of agentic AI systems that integrate large language models (LLMs), reasoning, planning, and acting abilities to facilitate radiology workflows. Agentic AI systems can be seen as the next level in transformer-based large language models that were previously incapable of self-interaction with devices, databases, and tasks outside text production. The review is based on existing knowledge regarding the foundational designs and functional mechanisms through multi-agent systems for the automation of radiomics workflows and interaction with the user, tools, and databases to show how agentic AI is far from the original one-step NLP applications. The review covers the applications in the automation of report writing, optimization, structured analysis, and workflow management with the added benefits of AI processability. However, the review also covers the challenges that come with the regulatory environments (such as regulatory issues regarding AI and medical devices), issues concerning privacy and security breaches, ethics, and sustainability issues arising from the mass adoption and use.

Outcomes and Implications

This review makes clear for clinicians that, although there is a role for agentic AI in performing advanced, complex radiology tasks, these remain early conceptual and developmental stages of their lives and are not yet ready or suitable for independent clinical deployment. Agentic systems might eventually streamline more repetitive workflow elements including draft reporting and structured data extraction. Their ability to orchestrate multistep tasks across tools and data sources will support interdisciplinary discussions or research workflows, particularly in high-volume imaging centers. On the other hand, transparent evidence about diagnostic accuracy, effects on reporting quality, or the direct impact on patient outcomes is lacking. Due to a lack of standardized evaluation metrics and regulatory approvals for agentic AI, clinicians should consider agentic systems as investigational aids rather than substitutes for expert judgment. Ethical and privacy considerations also constrain near-term utility in clinical settings. Investments in governance, cybersecurity safeguards, sustainability-compute cost and environmental footprint-and integration with existing radiology information systems are also essential before the safe and effective adoption of agentic AI.