Clinicians’ Experiences and Meanings in Using Artificial Intelligence for Healthcare Decision-Making
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Abstract
The integration of artificial intelligence (AI) in healthcare has reshaped clinical practice by transforming how medical professionals interact with information, make decisions, and deliver patient care. Within this technological shift, understanding the lived experiences of clinicians who engage with AI-based decision systems has become essential to addressing the human dimension of digital medicine. However, current research remains largely technocentric, focusing on system performance rather than the subjective meanings and ethical reflections that emerge through clinicians’ interactions with intelligent technologies. To address this gap, the present study employs a clearly defined hermeneutic phenomenological design, specifying procedural rigor in sampling, data collection, and analysis. Here, we apply a hermeneutic phenomenological approach to explore how healthcare professionals experience, interpret, and assign meaning to their engagement with AI systems in everyday clinical contexts. Data were collected through in-depth semi-structured interviews with twelve clinicians who regularly use AI-assisted diagnostic or treatment tools, followed by interpretative phenomenological analysis (IPA). The findings reveal that clinicians’ experiences are characterized by four interrelated themes: negotiating trust, emotional and ethical tension, adaptive learning, and reclaiming human connection in digital care. These themes highlight a continuous process of meaning-making in which clinicians navigate the coexistence of human intuition and machine reasoning, redefining their sense of professional identity and moral accountability. The study expands our understanding of human-AI collaboration by emphasizing that technological integration is not only procedural but also existential, requiring reflection on the social, ethical, and emotional dimensions of care. These insights provide a foundation for developing more human-centered AI systems that align innovation with empathy in healthcare.
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