Abstract Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to provide broad exposure to a wide variety of conditions, including those that are rare, subtle, or easily confused with one another. Although artificial intelligence (AI) has shown impressive performance in medical image classification, its potential to actively improve clinical education has not been fully realised. In this study, we introd