Artificial intelligence-enabled diagnostics promise to transform hearing healthcare, yet real-world adoption remains limited. This study identifies and prioritizes barriers to AI integration in clinical audiology through a three-phase mixed-methods approach. Phase I reviewed literature, surfacing 20 obstacles across workflow, infrastructure, culture, and ethics. Phase II involved expert interviews, refining these into nine context-specific barriers. In Phase III, a fuzzy-DEMATEL survey and thematic coding revealed a causal hierarchy: algorithmic inaccuracy, privacy concerns, and lack of training erode clinician trust and widen the knowledge gap. Cost, integration issues, and resource limitations add systemic stress, while ethical concerns emerge downstream. Cluster analysis groups the barriers into three blocs: Clinical Workflow, Governance and Trust, and Tech Infrastructure. This is the first study to apply fuzzy-DEMATEL to AI barriers in audiology, producing a causal map and cluster framework that offer both theoretical insights and practical guidance for adoption strategies.

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.

This study examines the issues driving the non-adoption of AI-based hearing aids. The gap is critical since, despite increasingly improving technology, particularly with the integration of artificial intelligence (AI), the persistent non-adoption raises questions about the technology’s appeal and usefulness. The study uses a qualitative research methodology, collecting data from the study participants through written responses to interview questions. The data was collected from 56 people with hearing impairment and analysed using the grounded theory methodology to identify the barriers. The selective codes, representing the five themes (barriers) that broadly constitute resistance, are (i) technological performance barriers, (ii) financial and functional barriers, (iii) privacy and data security apprehensions, (iv) control and autonomy concerns, and (v) social and health concerns.

Purpose – This study aims to examine clinicians’ perspectives on the diffusion, adoption and routinization of artificial intelligence (AI) use in hearing care organizations. While clinicians act as gatekeepers of technology adoption, they also engage with AI-generated outputs as a form of knowledge that must be evaluated, validated and integrated into clinical practice. By focusing on how clinicians make sense of and use AI-generated knowledge, the study addresses the limited understanding of post-adoption routinization of AI in healthcare settings. Design/methodology/approach – The study adopts a qualitative research design, using open-ended essays to capture hearing care clinicians’ lived professional experiences. The collected textual data were analyzed using an inductive-abductive approach. Guided by diffusion of innovation (DOI) theory, the study examined how clinicians engage with AI-generated knowledge across stages of adoption and sustained use. Findings – The analysis uncovers the drivers of diffusion, adoption and routinization of AI-enabled innovations, and maps them to five DOI innovation attributes. Specifically, it identifies drivers such as diagnostic precision, workflow integration, hands-on experimentation and quantifiable patient outcomes that explain how clinicians evaluate, validate and integrate AI-generated insights into everyday clinical decision-making. Originality/value – This study contributes to the knowledge management literature by showing that AI diffusion, adoption and routinization can be understood as processes of knowledge evaluation, integration and stabilization within professional practice. In addition, by extending DOI through a knowledge lens, the study offers a novel clinician-centered framework that explains how AI-generated knowledge is embedded in routine decision-making in knowledge-intensive settings.