Agentic AI in Radiology: Fundamentals, Drivers, and Gaps

Agentic artificial intelligence (AI) represents an emerging paradigm aimed at overcoming the limitations of current narrow, task-specific AI applications in radiology by enabling coordinated systems that integrate multiple tools, data sources, and workflows. Unlike isolated algorithms, Agentic AI systems are designed to manage complex clinical tasks through multimodal data integration, incorporation of longitudinal patient information, and dynamic coordination of diverse system components. This review provides a structured overview of the fundamental principles of Agentic AI and examines its potential relevance to radiological practice.

A narrative review was conducted to summarize the key concepts, architectures, and operational mechanisms underlying Agentic AI. Particular emphasis was placed on orchestration strategies, tool integration, multimodal data processing, and emerging system designs relevant to radiology. The review also examined the principal factors driving the development of Agentic AI and the barriers limiting its clinical adoption.

Several factors are accelerating interest in Agentic AI, including increasing clinical complexity, fragmented healthcare data ecosystems, and the growing need for longitudinal patient management. Emerging architecture demonstrates the potential to coordinate multiple AI tools and clinical information sources within unified workflows. However, current applications remain largely confined to experimental environments and lack extensive real-world validation. Significant challenges persist, including issues related to reliability, scalability, workflow integration, explainability, bias, regulatory compliance, and governance.

Agentic AI offers a promising framework for advancing the integration of AI into radiological practice by enabling coordinated, context-aware, and multimodal clinical workflows. Nevertheless, successful implementation will require rigorous clinical validation, standardized deployment strategies, robust governance frameworks, and close alignment with existing radiology workflows before widespread adoption can be achieved.