Toward truly agentic radiology AI: A multimodal architecture framework and differentiated implementation roadmap

Agentic artificial intelligence(AI) represents a structural transition from task-specific tools to autonomous systems capable of goal-oriented, multi-step reasoning. However, translating this potential into radiology requires a fundamental shift from text-dominated architectures to avision-first approachthat engages the specialty's diagnostic core. In this paper, we characterize the multimodal challenge distinguishing radiology from text-centric domains, where high-dimensional images serve as the primary diagnostic substrate rather than supplementary data. We reviewthree image-text coupling architectures, their clinical capabilities, limitations, and deployment feasibility. These include output-level coupling through sequential image-text pipelines, representation-level coupling through contrastive aligned dual encoders, and token-level coupling through generative vision-language models. To guide development, we introduce afive-level taxonomy of autonomy(Levels 1-5), establishing clear validation checkpoints and regulatory expectations for increasing machine independence, from text-based workflow automation to fully autonomous closed-loop systems. We emphasize that interoperability via standardized protocols, includingDICOMweb, HL7-FHIR, Model Context Protocol (MCP), and Agent2Agent (A2A), is as critical as the AI models themselves. Finally, we address critical implementation guardrails, including mitigatingalgorithmic hallucinations, defending againstadversarial prompt injection, and preserving human diagnostic proficiency amidst the risk of AI-induced skill decay. This comprehensive roadmap provides a structured path for integrating autonomous visual reasoning into modern medical imaging environments.