Exploring the Adoption of AI Hearing Care

Abstract

This paper explores the key enablers driving clinical adoption of AI-enabled audiology tools. Despite rapid technological maturity, real-world uptake remains uneven. Using a three-phase mixed-methods design, the authors identified and prioritised adoption drivers. Phase I involved a targeted literature scan (1991–2025), yielding 21 candidate factors. Phase II refined these through interviews with audiologists, physicians, engineers, and health-IT managers, distilling 10 core drivers. Phase III applied a decision-making trial and evaluation laboratory survey with 27 clinicians, revealing a causal chain where data accuracy, real-time analytics, and seamless integration enhance workflow efficiency, clinician confidence, and patient personalisation. Robust technical support and structured training further amplify adoption. Cluster analysis grouped drivers into technological, human-capital, and process domains, suggesting distinct tactical interventions. This study provides the first domain-specific causal influence map for AI adoption in hearing healthcare.

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