This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/92363
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The role of Artificial Intelligence in supporting Healthcare Workers’ Mental Well-being: A scoping review.
ABSTRACT
Background:
Artificial intelligence (AI) is poised to transform healthcare practice. Improving and supporting healthcare workers’ experience has become a priority for strengthening the healthcare delivery system. Mental health challenges faced by healthcare workers have gained recognition over the last decade, underscoring the need for continued exploration and action.
Objective:
This scoping review aimed to explore the role of AI in enhancing or supporting the mental health and well-being of healthcare workers.
Methods:
This scoping review was conducted following the Joanna Briggs Institute methodology and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The review included studies that directly addressed healthcare workers’ mental health and well-being and excluded those focusing on the indirect application of AI, such as through workflow optimisation. Academic and grey literature were searched with no date limits. A two-stage dual screening process was undertaken through Covidence. A customised data extraction tool was developed to extract data from the included studies, which were summarised descriptively.
Results:
Of 6,629 records identified, five studies met eligibility criteria. AI tools, such as deep learning, Natural Language Processing, Machine Learning, and Neural Networks, appear or are proposed to support mental health outcomes through personalised psychological support, screening and prediction of mental health issues. Burnout was the primary mental health issue, followed by anxiety, depression, and post-traumatic stress disorder. Nurses were the most prominently studied group. Evidence from empirical studies showed reductions in burnout and stress, while chatbot-based interventions demonstrated potential, but were constrained by small samples, heterogeneous designs, and short follow-up periods The conceptual framework outlined an AI-human model designed to deliver empathy-driven psychological support. Available empirical evidence remains limited in scope, and generalisability.
Conclusions:
Current research suggests that AI may have the potential to address mental health concerns among healthcare workers, particularly burnout, but the evidence base remains limited and largely conceptual. Robust longitudinal studies and supportive policy frameworks are needed to assess effectiveness, guide responsible implementation, and enable meaningful integration of AI-driven mental health support within healthcare systems. Clinical Trial: Not Applicable
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.