A Taxonomy of AI Interpretation Limitations in Medical Imaging: Archetypal Behaviors, Clinical Context, and Mitigation Strategies

Purpose: Artificial intelligence (AI) systems are increasingly deployed in clinical radiology, yet recurrent patterns of misinterpretation have emerged during real-world use. This study proposes a clinically relevant taxonomy to characterize recurring AI failure modes in medical imaging.

Methods/Materials: A pictorial narrative review was performed across multiple imaging modalities and radiology subspecialties, synthesizing recurrent limitations observed in radiologic AI systems. Failure patterns were categorized based on shared mechanisms related to data representation, contextual awareness, confidence calibration, and workflow integration. Representative imaging examples were selected to illustrate each category. Corresponding mitigation strategies were identified through analysis of system design principles and existing literature.

Results: AI misinterpretation patterns were identified into seven archetypes: false positive detection, missed or underdetected pathology, loss of clinical or temporal context, anatomic or developmental mislocalization, artifact-driven misclassification, miscalibrated certainty and automation bias, and human AI interaction or workflow-related failures. These archetypes were consistently observed across modalities and clinical scenarios. Each category demonstrated characteristic technical and contextual contributors, including limited dataset diversity, lack of longitudinal inputs, artifact sensitivity, inadequate uncertainty modeling, and suboptimal integration into radiologist workflows. Targeted mitigation strategies were mapped to each archetype.

Conclusions: Artificial intelligence systems in radiology exhibit consistent and recognizable patterns of misinterpretation shaped by their training paradigms and clinical deployment environments. Organizing these limitations into a structured taxonomy provides a practical framework for anticipating and mitigating artificial intelligence failures in clinical practice. Recognition of these archetypal behaviors supports informed human oversight and guides the development of artificial intelligence systems better aligned with the contextual and interpretive demands of diagnostic radiology.