Artificial Intelligence-Driven Iterative Design Framework for Optimizing User Experience in Medical Device Interfaces: A Cross-Disciplinary Empirical Study

Abstract

The increasing complexity of medical devices presents significant challenges to user interface (UI) design, directly impacting clinical efficiency and patient safety. Traditional design methodologies often struggle to keep pace with technological advancements and fail to adequately integrate user-centered principles, resulting in suboptimal user experiences. To address this gap, this study introduces and validates an Artificial Intelligence-Driven Iterative Design Framework (AIDF), a novel approach that synergizes machine learning, eye-tracking technology, and design innovation to systematically enhance the usability of medical device interfaces. The research involved an empirical study with 128 participants, including medical professionals and patients, who interacted with different UI prototypes of a simulated infusion pump. By leveraging a Convolutional Neural Network (CNN) to analyze eye-tracking data, the AIDF identified critical usability bottlenecks and generated data-driven design recommendations. The results demonstrate that the application of the AIDF led to a 42% reduction in task completion time, a 58% decrease in error rates, and a 35% improvement in user satisfaction scores compared to conventional design methods. These findings underscore the profound potential of integrating artificial intelligence into the design process, offering a robust, efficient, and scalable methodology for creating safer and more intuitive medical devices. This research provides a significant theoretical contribution to the field of human-computer interaction and offers practical guidance for the medical device industry, paving the way for next-generation, user-centric healthcare technologies.