Interpreting lines and tubes on chest X-rays (CXRs) is a critical skill that is often underrepresented in traditional medical curricula. To address this educational gap, we developed a mobile game-based e-learning package that integrates a clinically inspired narrative. In this game, students enter as a miniaturized radiologist navigating a doctor’s office maze. To “return to normal”, they must solve different radiological image interpretation challenges. Radiology can sometimes be perceived as abstract or technical, and narrative immersion can make the learning experience more engaging. The game comprises different levels, each with unique gameplay, such as matching cards, multiple choice questions, and reviewing chest X-rays. Increasing levels of difficulty and reward systems (e.g., power-ups) enhance the student’s motivation. A state-of-the-art artificial intelligence (AI) model trained and validated for different lines and tubes evaluates and provides feedback for students in certain levels. This simulates expert review through feedback and promotes reflective learning. A pilot trial including participants with various levels of clinical background showed that the e-learning package was effective in increasing participants' understanding of the topic by over 50%, with participants showing up to 70% increase in their confidence and interest in the topic material. Qualitative feedback showed the game’s clinical relevance, engagement, and user-friendly design. Our findings suggest that a game-based approach can enhance student learning in an immersive, accessible, and enjoyable format. Our games can be utilised by a broad range of students, making them efficient resources for radiology education.

Keywords: Artificial intelligence (AI), gamification, e-learning, interactive learning, medical imaging.