Review
AI-augmented rheumatologist? Reflections on AI-induced technostress, job insecurity and professional identity
M. Krasselt1
- Rheumatology, Medical Clinic III: Endocrinology, Nephrology and Rheumatology, Department of Internal Medicine, Neurology and Dermatology, University of Leipzig Medical Centre, Leipzig, Germany. marco.krasselt@medizin.uni-leipzig.de
CER20103
Review
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PMID: 42544611 [PubMed]
Received: 03/05/2026
Accepted : 26/06/2026
In Press: 29/07/2026
Abstract
OBJECTIVES:
To synthesise current evidence on artificial intelligence (AI) in rheumatology with a specific focus on AI-induced technostress, job insecurity and professional identity, and to propose a pragmatic framework for an ‘AI-augmented’ rheumatologist.
METHODS:
A narrative review was conducted using focused searches for articles published between January 2015 and March 2026. Search terms combined AI- and rheumatology-related concepts with terms related to technostress, burnout and professional identity. Reference lists of key rheumatology AI reviews and empirical studies were screened for additional publications. Eligible works included empirical studies, reviews and conceptual papers on AI in rheumatology or AI-related technostress and psychological or professional consequences for physicians.
RESULTS:
Current AI applications in rheumatology span imaging, risk prediction, data integration and large language model (LLM)-based tools, but routine use remains low despite largely positive expectations among rheumatologists and patients. Evidence from mixed-specialty cohorts indicates that AI-related self-esteem threat is a central driver of job insecurity and may contribute to burnout. Rheumatology’s reliance on longitudinal pattern recognition, uncertainty management and relationship-centred care makes perceived threats to expertise particularly salient, although similar dynamics likely affect other specialties. Building on this evidence, a four-pillar framework for an ‘AI-augmented’ rheumatologist is proposed: clear role boundaries between humans and AI, rheumatology-specific AI literacy, clinician- and teamled implementation and governance, and routine monitoring of technostress and well-being.
CONCLUSIONS:
AI can meaningfully support rheumatology care and, when well-designed and implemented, may even reduce certain forms of technostress by offloading administrative and repetitive tasks. At the same time, AI-related technostress, perceived self-esteem threat and job insecurity can undermine professional identity and wellbeing if introduced without attention to role boundaries, governance and multiprofessional collaboration. The proposed framework aims to help rheumatologists and institutions integrate AI in ways that preserve core professional values and clinician well-being.
