Towards Trustable Intelligent Clinical Decision Support Systems: A User Study with Ophthalmologists

DOI

Integrating Artificial Intelligence (AI) into Clinical Decision Support Systems (CDSS) presents significant opportunities for improving healthcare delivery, particularly in fields like ophthalmology. This paper explores the usability and trustworthiness of an AI-driven CDSS designed to assist ophthalmologists in treating diabetic retinopathy and age-related macular degeneration. Therefore, we created a CDSS and evaluated its impact on efficiency, informedness, and user experience through task-based semi-structured interviews and questionnaires with 11 ophthalmologists. The usability of the CDSS was rated highly, with a SUS of 81.75. Additionally, results show that participants felt like the CDSS would improve their efficiency and informedness with one major aspect being integrating Electronic Health Records (EHR) and Optical Coherence Tomography (OCT) data into a single interface. Additionally, we explored aspects of the trustworthiness of AI components, specifically OCT segmentation, treatment recommendation, and visual acuity forecasting. Through thematic analysis, we identified key factors influencing trustworthiness and clinical adoption. Results show that a larger degree of abstraction from input to output of a model correlates with decreased trust. From our findings, we propose two guidelines for designing trustworthy CDSS.