A Framework for Identifying and Understanding Cognitive Biases in Artificial Intelligence-Assisted Radiology.

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Abstract

Artificial intelligence (AI) creates new susceptibility to diagnostic error in radiology related to the cognitive processes by which radiologists' interpretations incorporate AI outputs. This Perspective presents a conceptual framework for cognitive biases in AI-assisted radiology linking specific biases to radiologist-AI interactions. The framework uses dual-process theory, recognizing fast intuitive and deliberate analytic modes of information processing, as an organizing model for understanding cognitive processes underlying radiologists' decisions and their potential contribution to bias. Across seven decision scenarios spanning image viewing, findings interpretation, and contextual integration, the framework examines how AI output timing, radiologist-AI agreement, trust calibration, and reader expertise influence decision-making. We identify adaptive heuristics that support efficient decisions but, when misapplied or overrelied upon, increase susceptibility to complacency, confirmation, automation, distrust, anchoring, and usual implication bias, and increase behavioral risk of use without qualification. We highlight AI paradoxes emerging from the framework of despecialization, system fragility, and deskilling. Finally, we propose strategies targeting these cognitive biases, including education and experiential learning, workflow and interface design, clinical oversight, and performance monitoring. This framework provides a structured basis for generating testable hypotheses and designing AI-assisted radiology systems that preserve the strengths of human expertise and AI while supporting calibrated reliance.

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