Negotiating Trust, Identity, and Responsibility: Clinicians’ Experiences with AI-Assisted Diagnosis in Digital Healthcare Settings

Abstract

Artificial intelligence (AI) is reshaping clinical practice by supporting diagnostic decision-making, prompting new questions about how healthcare professionals experience and adapt to this technological integration. While much attention has been paid to the performance of AI systems, less is known about the subjective experiences of clinicians navigating this evolving landscape. Current literature lacks insight into how professionals construct meaning around trust, responsibility, and identity in AI-mediated diagnostic settings.

This study adopts an interpretative phenomenological approach (IPA) to explore how clinicians make sense of their interactions with AI in digital hospital environments. Using in-depth, semi-structured interviews with nine healthcare professionals, the study identified three central themes: conditional trust in AI, redefinition of professional identity, and ethical tensions surrounding accountability. Data were analyzed through a multi-step IPA process, focusing on meaning units, emergent themes, and cross-participant convergence. These themes reveal how participants continuously negotiate their role in a system that challenges traditional models of expertise and decision-making. The results show that AI is not merely a tool but a transformative agent that affects the clinician’s sense of agency and moral responsibility.

These findings contribute to a more holistic understanding of AI in healthcare by emphasizing the experiential and ethical dimensions often overlooked in technical assessments. The study suggests that future research and implementation efforts must account for the lived experiences of clinicians to ensure responsible, human-centered AI integration in medical practice.