BURNOUT AND AUTOMATION IN HEALTHCARE: THE PSYCHOSOCIAL CONSEQUENCES OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY - Aleksandra Tołkacz; Aleksandra Kosikowska; Maciej Tołkacz; Aleksandra Kwiatkowska (International Journal of Innovative Technologies in Social Science, 2026)
Background: Diagnostic imaging is characterized by increasing workloads, workforce shortages, and chronic time pressure, contributing to high occupational burnout among radiologists. Concurrently, artificial intelligence (AI) is rapidly entering clinical workflows, yet its psychosocial impact on clinician well-being remains insufficiently understood. Aim: To critically analyze occupational burnout among radiologists during digital healthcare transformation, emphasizing how AI may shape psychosocial working conditions, cognitive demands, and professional autonomy. Methods: A narrative review was conducted using PubMed, Scopus, and Google Scholar (2020 - May 2026). Peer-reviewed studies addressing organizational, behavioral, and psychological aspects of AI and digital transformation in radiology were included. Studies focusing solely on technical performance without human-factor relevance were excluded. Results: Occupational burnout in radiology is described as a chronic, systemic phenomenon associated with workload pressure, workforce shortages, and organizational strain. AI applications may support workflow efficiency; however, they also alter cognitive demands, including increased system supervision and decision integration tasks. The long-term impact of AI on burnout remains uncertain. Conclusions: AI should be considered a sociotechnical factor with both potential benefits and risks for clinician well-being. Burnout mitigation requires integrated, system-level interventions and Human-Centered AI approaches, though empirical validation of their impact on burnout outcomes remains limited.