AI in Radiology: Shaping the Present, Defining the Future

Abstract

We are in the midst of a digital transformation in healthcare, catalyzed by artificial intelligence (AI), with radiology at the forefront. The integration of AI into radiology presents transformative potential, yet its adoption remains challenged by technical, organizational, and translational obstacles. As healthcare increasingly embraces AI, radiology—an inherently data-rich specialty—faces unique demands to align AI-driven workflows with institutional strategies and priorities, clinical needs, patient expectations, and regulatory constraints. Despite rapid advancements, the translation of AI from development to routine clinical practice has been slower than anticipated, primarily due to issues related to cost, data infrastructure, change management, and trust. Achieving successful AI implementation in radiology requires a comprehensive approach that includes enforcing responsible practices to ensure safety and accountability, as well as maintaining unified data platforms to support scalability and interoperability. Current radiology AI applications range from interpretive tools for diagnostic triage to non-interpretive solutions that optimize workflow and communication. Future opportunities lie in leveraging multimodal AI and agentic models to bridge healthcare disparities while enhancing diagnostic and operational performance.

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