Expertise Matters in AI Adoption: A Comparative Study of Retina Specialists and General Ophthalmologists in AI-CAD Adoption

Abstract

AI-based computer-aided diagnosis (AI-CAD) systems are transforming medical imaging by augmenting clinicians in disease identification and diagnosis. Nonetheless, little is known about how individual differences, particularly clinicians’ expertise, affect their perception, trust, and adoption of such systems. Guided by the Elaborated Likelihood Model (ELM), this study systematically compared Task Experts (TEs; retina specialists; <i>n</i> = 38) and Task Non-Experts (TNs; general ophthalmologists; <i>n</i> = 23). TNs reported higher scores than TEs across all adoption metrics, including perceived accuracy, interpretability, credibility, ease of use, usefulness, and intention to use. For further investigation of underlying cognitive processes, PLS-SEM was conducted. It revealed that perceived usefulness was the sole direct predictor of intention to use in both groups, yet its antecedents differed by expertise. Perceived accuracy and interpretability strongly influenced TEs, reflecting central-route processing, whereas AI optimism shaped TNs’ attitude, reflecting peripheral-route processing. These findings highlight the need for considering clinicians’ expertise levels in AI-CAD design.

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