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This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists\u2019 own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. 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