Development of an AI-based interactive tool to support radiographer training in chest x-ray analysis

Abstract

The increasing demand for chest X-ray examinations presents challenges in radiography education, requiring scalable and interactive learning solutions. This study presents an AI-based interactive tool designed to enhance radiographer training by providing real-time feedback, anatomical segmentation, and self-assessment features. Twenty-five third-year radiography students evaluated the tool’s usability and perceived quality using a validated evaluation framework. The results indicate high learnability (3.12/4), system response time (3.54/4), and security (3.38/4) but highlight areas for improvement in stability (2.79/4) and diagnostic performance (2.79/4). The tool was generally well accepted, with moderate scores perceived benefit (3.02/4) and intention of use (2.75/4). While the AI tool shows promise in enhancing radiography education through interactive learning, further improvements in stability and user interface design are needed for broader adoption. Future studies will assess its impact on learning outcomes and clinical decision-making skills.

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