Clinicians’ Meaning-Making of AI-Based Decision Support in Telemedicine
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Abstract
The rapid integration of artificial intelligence into telemedicine has reshaped clinical decision-making within the broader field of medical informatics, emphasizing efficiency, scalability, and data-driven care. Within this context, AI-based clinical decision support systems are increasingly embedded in remote consultations, yet their role is often understood through technical performance rather than clinicians’ lived experiences of using them in practice. What remains insufficiently understood is how clinicians subjectively experience, interpret, and integrate AI-generated recommendations during telemedicine encounters, and how these experiences shape clinical judgment. Here, an interpretative phenomenological approach is used to explore clinicians’ lived experiences . This qualitative study involved 15 clinicians (8 physicians and 7 nurse practitioners) working in hospital- and primary care-based telemedicine services who had at least one year of experience using AI-supported clinical decision support systems. Participants were recruited through purposive sampling to ensure direct and sustained engagement with AI tools in routine remote consultations. Data were generated through in-depth semi-structured interviews conducted online between March and June 2025, each lasting 60–90 minutes, and were analyzed using Interpretative Phenomenological Analysis following a systematic process of iterative coding, case-by-case analysis, and cross-case thematic development to ensure analytic rigor and transparency. The analysis identified themes that reflect clinicians’ ongoing negotiation between trust and skepticism, ethical responsibility, workflow reconfiguration, and the preservation of the doctor–patient relationship. These findings show that AI functions as an interpretive partner that intensifies reflective judgment rather than replacing professional authority. These insights advance understanding of AI-supported telemedicine by foregrounding clinicians’ meaning-making processes and highlight the need for human-centered AI design and future research that further explores experiential dimensions of digital healthcare.
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