Measuring the rate of machine hallucination is critical to assessing the utility and trustworthiness of generative AI deployed in radiology; however, there are multiple widely used notions of what constitutes hallucination. This ambiguity pervades industry, academia, and regulatory discourse, such that even an experienced radiologist cannot be certain that a generative model has been evaluated using a definition of hallucination aligned with clinical use. As a result, the same generative AI system may be characterized as either never hallucinating or always hallucinating, depending on perspective. This article provides a brief, non-technical explanation of the fundamental disparity between two seemingly incongruous notions of machine hallucination. Using terms familiar to radiologists, a taxonomy of machine hallucination is proposed that explicitly delineates output contingencies for deployed AI systems. By clarifying what constitutes hallucination under different interpretations, this framework aims to reduce ambiguity and facilitate clearer communication among users, developers, vendors, and administrators regarding the performance of generative AI in radiology. © RSNA, 2026.
Brooks et al. (Wed,) studied this question.
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