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Resistance, adaptation, and irreducible paradox. How AI reshapes clinical professional identity: A meta-narrative
Francesca Guerra1, Maartje Schermer1, Jacob J Visser2
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands.
Abstract:
Artificial intelligence (AI) is increasingly embedded in clinical workflows; however, research on its impact on healthcare professionals' identity yields contradictory findings, from threat and deskilling to empowerment and repositioning. These contradictions persist because studies typically operate within single theoretical paradigms, each capturing only one facet of a multidimensional phenomenon. This meta-narrative systematic review, following RAMESES guidelines, synthesises 46 studies across five databases to map how different research traditions conceptualise AI's impact and explain why the literature produces incompatible conclusions. The synthesis identifies six theoretical traditions - sociology of professions, organisational and sociotechnical implementation, behavioural and cognitive acceptance, clinical practice and reflective knowledge, discursive and critical constructivist, and ethical, reflexive and governance - each revealing a distinct dimension of identity transformation. Across these traditions, three simultaneous cross-tradition themes emerge: resistance and defence, adaptation and transformation, and complexity and paradox. These are not sequential stages; they coexist within traditions, within studies, and within the same professionals, reflecting the multidimensional structure of professional identity rather than inconsistency. The findings extend existing frameworks by showing that AI reshapes identity work in three interconnected ways. First, it creates conditions of permanent provisionality, as professionals must continually renegotiate their roles and expertise. Second, it produces hollowed-out jurisdictions, in which formal professional authority remains intact while its epistemic basis increasingly shifts towards algorithmic systems. Third, it generates epistemic friction between clinical intuition and algorithmic reasoning. AI implementation in healthcare is fundamentally a professional identity transformation challenge requiring simultaneous engagement across jurisdictional, organisational, cognitive, practice-based, discursive, and ethical dimensions. In some clinical contexts, refraining from implementation may be the most appropriate response.
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