Clinicians’ Lived Experiences of Trust, Control, and Ethical Awareness in AI-Assisted Healthcare
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Abstract
Artificial Intelligence (AI) and Machine Learning (ML) have transformed healthcare by augmenting clinical decision-making and diagnostic precision, reshaping how clinicians interact with digital systems. Within this evolving landscape, understanding clinicians’ lived experiences with AI has become essential, as it reflects not only technological adaptation but also shifts in professional identity and ethical responsibility. However, despite extensive research on algorithmic performance and usability, little is known about how clinicians construct trust, agency, and emotional meaning when working alongside AI systems. This study employs a hermeneutic phenomenological approach to explore the lived experiences of clinicians who use AI-driven decision support tools, interpreting how they negotiate trust, control, and moral accountability in practice. Data were collected through semi-structured interviews with twelve clinicians and analyzed using Interpretative Phenomenological Analysis (IPA) to identify essential themes of trust formation, emotional ambivalence, professional adaptation, and ethical reflection. The findings reveal that trust in AI emerges as a dynamic, relational process grounded in emotional engagement and experiential validation rather than mere technical reliability. Moreover, clinicians experience a reconfiguration of their professional identity as they learn to coexist with AI systems, developing new forms of ethical awareness and cognitive alignment. In practical terms, the study highlights the need for healthcare organizations to design AI implementation strategies that support clinicians’ emotional engagement, ethical judgment, and sense of control—not solely their technical proficiency. Overall, this study offers a unique human-centered contribution by demonstrating that clinicians’ lived meanings and ethical orientations are central to achieving sustainable and responsible AI integration in healthcare.
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