A Multidimensional Approach to Usability Engineering, Clinical Performance and Human-AI Interaction in AI-Enabled Medical Devices

Abstract

Artificial Intelligence-Enabled Medical Devices (AIeMD) promise to revolutionize healthcare, yet their safe adoption relies on effective Human-AI Interaction (HAAI) design and validation. Established usability engineering standards and guidances, including IEC 62366-1 and FDA frameworks, fail to address the novel sociotechnical risks of "black-box" systems, including automation bias and the misalignment of clinician mental models. This doctoral project directly addresses this gap, with the primary goal of developing a tailored human factor/usability engineering framework specifically for AIeMD. The project aims to establish robust methodologies for evaluating transparency and trust, integrating human factors and clinical performance into a multidimensional validation pipeline. Ultimately, this work will provide the evaluative tools necessary to move from subjective satisfaction to safety-critical, risk-based validation, ensuring that AI-enabled health solutions are clinically reliable, transparent, and compliant