Exploring the Usability and Trustworthiness of AI-Driven User Interfaces for Neurological Diagnosis

This study explores the application of Artificial Intelligence (AI) in the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), through Human-Computer Interaction (HCI), Human-Centered AI (HCAI), and Explainable AI (XAI). It evaluates three user interfaces designed to integrate AI insights with the clinical understanding of neurologists, aiming to refine diagnostic processes. Neurology professionals were involved to gauge their knowledge and confidence in the AI-supported diagnoses. Utilizing a remotely administered questionnaire, this research investigates clinicians' views on XAI outputs, focusing on how results are visualized and their ability to engender trust in AI's clinical utility. This method emphasizes the importance of clear, trustworthy AI systems in healthcare and underscores the essential role of effective human-AI collaboration in enhancing patient care and diagnostic precision.