Human–AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation

Abstract

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction with radiologists in clinical workflows. This narrative review summarizes current knowledge on human-AI interaction in radiology and highlights practical risks and opportunities for clinical teams. First, simple conceptual models of human-AI collaboration are described, such as diagnostic complementarity, which explain when radiologists and AI can achieve synergistic performance exceeding that of either alone. Then, AI tool integration strategies along the imaging pathway are reviewed, from acquisition and triage to interpretation, reporting, and teaching, outlining common interaction models and physician-in-the-loop workflows. Cognitive and professional effects of AI integration are also discussed, including automation bias, algorithmic aversion, deskilling, workload management, and burnout, with specific vulnerabilities for trainees. Furthermore, key elements of responsible implementation are summarized, such as liability and oversight implications, continuous monitoring for performance drift, usable explanations, basic AI literacy, and co-design with radiology teams. Finally, emerging systems are introduced, including vision-language models and adaptive learning loops. This review aims to provide a clear and accessible overview to help the radiology community recognize where human-AI collaboration can add value, where it can cause harm, and which questions future studies must address.

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