The artificial intelligence (AI) competence paradox: how AI reshapes clinical expertise

Purpose This study aims to examine how health-care professionals perceive artificial intelligence’s (AI) impact on clinical competencies. Design/methodology/approach A qualitative methodology using semi-structured interviews with health-care professionals and AI experts. Findings Clinicians reported no deskilling with current AI applications, primarily attributing this to their narrow scope and pre-AI clinical training. However, they expressed concerns about deskilling risks for future generations of health-care professionals as AI capabilities expand and become more deeply integrated into clinical practice. A notable shift emerged in how competence is conceptualised: deskilling concerns centred on individual capabilities, while upskilling was framed through collective human–AI performance. Research limitations/implications The research is based on the specific context of the Swedish health-care system. Organisational structures and regulations may affect the transferability of results to other countries and systems. The purposive sampling method was designed to identify participants with practical experience of AI-driven automation in clinical settings. Few health-care providers in Sweden are working with AI today. This limits the selection of participants, but above all, it is often tech enthusiasts and enthusiasts who work with AI. These risks create a distorted picture that potentially creates a self-selection bias may underrepresent more critical perspectives on AI integration. Practical implications Health-care organisations face a critical but narrow window of opportunity to shape AI integration effectively. While current clinical competencies remain stable, this period of stability is likely temporary. Organisations must proactively identify core clinical competencies to preserve and develop targeted strategies to protect these skills. Key actions include strategic decisions about what to automate, human-centred system design, redesigned training programs and implementation of safeguards to prevent erosion of essential clinical expertise. These findings provide health-care leaders with a framework for managing the transition to AI-enhanced practice while preserving critical medical competencies. Social implications Patients’ trust and relationship with doctors are a cornerstone of health care. As AI is increasingly incorporated into health care, it is crucial for society that essential human elements such as trust and empathy are not disrupted, but rather developed. Originality/value This study reveals how AI integration shifts focus from individual to collective capabilities. It advances the understanding of how AI integration affects professional expertise by revealing the complex interplay between individual clinical competencies and emerging forms of human–AI collaboration.