Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review
Abstract
Background Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depression, anxiety, burnout and other conditions amongst various occupational groups. Novel approaches are needed to support clinician well-being. Objective This integrative literature review aims to explore the current state of research examining the use of generative artificial intelligence (GenAI) and machine learning (ML) systems to predict mental health issues and identify associated risk factors amongst healthcare professionals. Methods A literature search of databases was conducted in Medline then adapted as necessary to Scopus, Web of Science, Google Scholar, PubMed and CINAHL with Full Text. Eleven studies met the inclusion criteria for the review. Results Nine studies employed various machine learning techniques to predict different mental health outcomes among healthcare workers. Models showed good predictive performance, with AUCs ranging from 0.82 to 0.904 for outcomes such as depression, anxiety and safety perceptions. Key risk factors identified included fatigue, stress, burnout, workload, sleep issues and lack of support. Two studies explored the potential of sensor-based technologies and GenAI analysis of physiological data. None of the included studies focused on the use of GenAI systems specifically for providing mental health support to healthcare workers. Conclusion Preliminary research demonstrates that AI/ML models can effectively predict mental health issues. However, more work is needed to evaluate the real-world integration and impact of these tools, including GenAI systems, in identifying clinician distress and supporting well-being over time. Further research should aim to explore how GenAI may be developed and applied to provide mental health support for healthcare workers.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing scholarly literature on GenAI's application in healthcare often focuses almost exclusively on technical performance indicators such as accuracy rates, time efficiency, and comparative error reduction against human benchmarks.
“Existing scholarly literature17–22 on GenAI’s application in healthcare often focuses almost exclusively on technical performance indicators such as accuracy rates, time efficiency, and comparative error reduction against human benchmarks.”
From Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications – A Systematic Review (2026) · cites this paper as [17]
Cites
The integrative review: updated methodology
Whittemore, R., & Knafl, K. (2005) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2005.03621.x · Cited by 10,241 · see citing works
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works
Generative AI and ChatGPT: applications, challenges, and AI-human collaboration
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2023.2233814 · Cited by 1,363 · see citing works
Scott, A. J., Tan, B. Y., Huak, C. Y., Sia, C.-H., Chua, Y. X., Goh, E. T., Zbikowska, G., Dykowska, G., Martin, G., Kinross, J. K., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0238666 · Cited by 500 · see citing works
Saragih, I. D., Tonapa, S. I., Saragih, I. S., Advani, S., Batubara, S. O., Suarilah, I., & Lin, C. (2021) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.104002 · Cited by 427 · see citing works
Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2021) · Annals of Operations Research · doi:10.1007/s10479-020-03856-6 · Cited by 374 · see citing works
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278 · see citing works
Sai, S., Gaur, A., Sai, R., Chamola, V., Guizani, M., & Rodrigues, J. J. P. C. (2024) · IEEE Access · doi:10.1109/access.2024.3367715 · Cited by 268 · see citing works
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267 · see citing works
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105917 · Cited by 221 · see citing works
Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI
Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Shakeri Hossein Abad, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin, B., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01074-z · Cited by 213 · see citing works
Artificial intelligence: opportunities and implications for the health workforce
Hazarika, I. (2020) · International Health · doi:10.1093/inthealth/ihaa007 · Cited by 203 · see citing works
Chigwedere, O. C., Sadath, A., Kabir, Z., & Arensman, E. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136695 · Cited by 181 · see citing works
Shaikh, F., Afshan, G., Anwar, R. S., Abbas, Z., & Channa, K. A. (2023) · Asia Pacific Journal of Human Resources · doi:10.1111/1744-7941.12385 · Cited by 174 · see citing works
Wang, W., Chen, L., Xiong, M., & Wang, Y. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10154-4 · Cited by 173 · see citing works
Health care workers' mental health in the face of COVID-19: a systematic review and meta-analysis
Ghahramani, S., Kasraei, H., Hayati, R., Tabrizi, R., & Marzaleh, M. A. (2022) · International Journal of Psychiatry in Clinical Practice · doi:10.1080/13651501.2022.2101927 · Cited by 155 · see citing works
How can artificial intelligence decrease cognitive and work burden for front line practitioners?
Gandhi, T. K., Classen, D. C., Sinsky, C. A., Rhew, D. C., Garde, N. V., Roberts, A., & Federico, F. A. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad079 · Cited by 137 · see citing works
Gupta, S., Kamboj, S., & Bag, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10174-0 · Cited by 129 · see citing works
Yim, D., Khuntia, J., Parameswaran, V., & Meyers, A. D. (2024) · JMIR Medical Informatics · doi:10.2196/52073 · Cited by 129 · see citing works
Hao, Q., Wang, D., Xie, M., Tang, Y., Dou, Y., Zhu, L., Wu, Y., Dai, M., Wu, H., & Wang, Q. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.567381 · Cited by 128 · see citing works
The health risks of generative AI-based wellness apps
De Freitas, J., & Cohen, I. G. (2024) · Nature Medicine · doi:10.1038/s41591-024-02943-6 · Cited by 95 · see citing works
Elyoseph, Z., Levkovich, I., & Shinan‐Altman, S. (2024) · Family Medicine and Community Health · doi:10.1136/fmch-2023-002583 · Cited by 77 · see citing works
A scoping review on monitoring mental health using smart wearable devices
Long, N., Lei, Y., Peng, L., Xu, P., & Mao, P. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022369 · Cited by 72 · see citing works
King, D. R., Nanda, G., Stoddard, J., Dempsey, A., Hergert, S., Shore, J. H., & Torous, J. (2023) · Current Psychiatry Reports · doi:10.1007/s11920-023-01477-x · Cited by 63 · see citing works
Current and Future Challenges in the Delivery of Mental Healthcare during COVID-19
Gautam, M., Thakrar, A., Akinyemi, E., & Mahr, G. (2020) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-020-00348-3 · Cited by 63 · see citing works
The Ethics of Artificial Intelligence in the Era of Generative AI
Kirova, V. D., Ku, C. S., Laracy, J. R., & Marlowe, T. J. (2023) · Journal of systemics, cybernetics, and informatics/Journal of systemics cybernetics and informatics · doi:10.54808/jsci.21.04.42 · Cited by 54 · see citing works
Noble, J. M., Zamani, A., Gharaat, M., Merrick, D., Maeda, N., Lambe Foster, A., Nikolaidis, I., Goud, R., Stroulia, E., Agyapong, V. I. O., et al. (2022) · JMIR Research Protocols · doi:10.2196/33717 · Cited by 53 · see citing works
Predicting physician burnout using clinical activity logs: Model performance and lessons learned
Lou, S. S., Liu, H., Warner, B. C., Harford, D. R., Lu, C., & Kannampallil, T. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104015 · Cited by 51 · see citing works
The Artificial Third: Utilizing ChatGPT in Mental Health
Tal, A., Elyoseph, Z., Haber, Y., Angert, T., Gur, T., Simon, T., & Asman, O. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2250297 · Cited by 47 · see citing works
Alfonsi, V., Scarpelli, S., Gorgoni, M., Couyoumdjian, A., Rosiello, F., Sandroni, C., Corsi, R., Pietrantonio, F., & Gennaro, L. D. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021410 · Cited by 44 · see citing works
Rosen, S. R., & Saban, M. (2023) · European Radiology · doi:10.1007/s00330-023-10230-0 · Cited by 38 · see citing works
Self-citationTurchin, A., & Builes, L. F. F. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211000831 · Cited by 36 · see citing works
Dang, V. N., Cascarano, A., Mulder, R. H., Cecil, C. A. M., Zuluaga, M. A., Hernández-González, J., & Lekadir, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-58427-7 · Cited by 36 · see citing works
Havaei, F., Ji, X. R., MacPhee, M., & Straight, H. (2021) · BMC Nursing · doi:10.1186/s12912-021-00742-9 · Cited by 34 · see citing works
Szajna, A., & Kostrzewski, M. (2022) · Sustainability · doi:10.3390/su14116729 · Cited by 31 · see citing works
Applying machine learning on health record data from general practitioners to predict suicidality
van Mens, K., Elzinga, E., Nielen, M. M. J., Lokkerbol, J., Poortvliet, R., Donker, G., Heins, M. J., Korevaar, J. C., Dückers, M., Aussems, C., et al. (2020) · Internet Interventions · doi:10.1016/j.invent.2020.100337 · Cited by 28 · see citing works
Situmorang, D. D. B. (2022) · Journal of Public Health · doi:10.1093/pubmed/fdac159 · Cited by 26 · see citing works
Gupta, M. D., Bansal, A., Sarkar, P. G., Girish, M. P., Jha, M., Yusuf, J., Kumar, S., Kumar, S., Jain, A., Kathuria, S., et al. (2021) · Indian Heart Journal · doi:10.1016/j.ihj.2020.11.145 · Cited by 25 · see citing works
Zhou, Z., Luo, D., Yang, B. X., & Liu, Z. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.876995 · Cited by 23 · see citing works
Dolev, T., Zubedat, S., Brand, Z., Bloch, B., Mader, E., Blondheim, O., & Avital, A. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01299-6 · Cited by 22 · see citing works
Elyashar, A., Plochotnikov, I., Cohen, I., Puzis, R., & Cohen, O. (2021) · Journal of Medical Internet Research · doi:10.2196/30217 · Cited by 22 · see citing works
Revell, G. (2024) · Springer eBooks · doi:10.1007/978-3-031-46238-2_9 · Cited by 19 · see citing works
Turchi, G. P., Salvalaggio, I., Croce, C., Riva, M. S. D., Orrù, L., & Iudici, A. (2022) · Behavioral Sciences · doi:10.3390/bs12050134 · Cited by 11 · see citing works
Hammoud, B., Semaan, A. T., Elhajj, I. H., & Beňová, L. (2022) · Human Resources for Health · doi:10.1186/s12960-022-00758-5 · Cited by 10 · see citing works
Can ChatGPT assist with the initial triage? A case study of stroke in young females
Rosen, S., & Saban, M. (2023) · International Emergency Nursing · doi:10.1016/j.ienj.2023.101340 · Cited by 7 · see citing works
Self-citationMillard, D. W., Agulnik, P. L., Armstrong, N., Fees, C., Hall, J., Kennard, D., & Leach, J. (2022) · History of Psychiatry · doi:10.1177/0957154x221142416 · Cited by 5 · see citing works
Shu, L., Sznajder, K. K., Ning, L., Gao, H., Xie, X., Liu, S., Shao, C., Li, X., & Yang, X. (2023) · Journal of Nursing Management · doi:10.1155/2023/5524561 · Cited by 4 · see citing works
Almayyan, W. I. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5668888 · Cited by 3 · see citing works
Detecting Burnout of Health Care Professionals in a COVID-19 Testing Laboratory
Leite, C. C. M., Chronopoulou, A., Yuceel, A. I., & Wooldridge, A. R. (2022) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181322661066 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Elyoseph, Z., Levi‐Belz, Y., Levkovich, I., Haber, Y., Gramaglia, C. M., López‐Castromán, J., Hanon, C., & Olié, É. (2026) · BMC Psychiatry · doi:10.1186/s12888-025-07737-9 · Cited by 9 · see citing works
Artificial intelligence in medical physics
Amoroso, N., Errico, R., Pantaleo, E., Monaco, A., & Bellotti, R. (2025) · Rivista Del Nuovo Cimento · doi:10.1007/s40766-025-00073-4 · Cited by 5 · see citing works
Nguyen, M., Nham, T. P., Alghafes, R., Rahman, O., & Imre, G. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106271 · Cited by 3 · see citing works
Al-Anezi, F. M. (2026) · Journal of Healthcare Leadership · doi:10.2147/jhl.s590498 · Cited by 6 · see citing works
Son, N. K., Sangaiah, A. K., Paramarthika, L. K. M., Rajendran, V., Bian, G., & Alenazi, M. J. F. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3572596 · Cited by 3 · see citing works
Alves, E., Rodrigues, R., da Fonseca, C. J. V., Lopes, M. J., Faria, I., Dalkner, N., Barach, P. R., Folkvord, F., Carlson, J. I., Reininghaus, E. Z., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-097330 · Cited by 3 · see citing works
Li, J., Tang, Z., & Mahmoud, H. A. (2025) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2024.155658 · Cited by 3 · see citing works
Rocha, A., Costeira, C. R. B., Barbosa, R., Gonçalves, F., Castelo-Branco, M., Viana, J., Gaudêncio, M., & Ventura, F. I. Q. S. (2025) · BMC Nursing · doi:10.1186/s12912-025-03277-5 · Cited by 3 · see citing works
Navigating the labyrinth of healthcare workers' mental health
Zhang, H.-L., & Zhang, Q.-Y. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1699755 · Cited by 1 · see citing works
Duarte-Martínez, V., Zambrano-Arriaga, J., & Cobo, M. J. (2025) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg65568.2025.11081535
Nyaga, D., & Torres, R. A. (2025) · Journal of Ethnicity in Substance Abuse · doi:10.1080/15332640.2025.2548523
Descatha, A., Hawkins, D., Sembajwe, G. N., Aloui, S., Fadel, E., Gagliardi, D., Violante, F. S., Sim, M., Fadel, M., & Kang, S.-K. (2026) · Safety and Health at Work · doi:10.1016/j.shaw.2026.02.004
Sharma, S. K., Khan, A. R., Tejani, G. G., Bassir, D., & Tripathi, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001158
AI in foreign language learning and teaching: theory and practice
Liu, S., Wu, R., & Zhang, L. (2026) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2025-0036
Fong, T. C. T., Chan, C. H., Kwok, A. P. K., Chan, R. T. H., Tang, R. L. M., Wen, M., & YIP, P. S. F. (2026) · Journal of Medical Internet Research · doi:10.2196/88362
Subramani, K., & Manoharan, G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9119-9.ch009
Asmi, F., Wong, A., Almugren, I., Chotia, V., & Bilinovics–Sipos, J. (2026) · Journal of Enterprise Information Management · doi:10.1108/jeim-10-2025-1027
- Authors
- Levin, C., Naimi, E., & Saban, M.
- Affiliations
- Jerusalem College of Technology (IL)
- Tel Aviv University (IL)
- Corresponding author
- Saban, M.
- ORCID
- Saban, M.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2024-11
- Volume
- 191
- Pages
- 105566
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W4401159027
- Cited by
- 17
- References
- 53
- Citations by year
- 2026: 9 · 2025: 8
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, machine learning, mental health prediction, healthcare workers, clinician well-being, burnout, depression, anxiety, occupational stress, workload, risk factors, sensor-based monitoring, physiological signals, clinician distress
- MeSH terms
- Machine Learning, Anxiety, Artificial Intelligence, Mental Disorders, Burnout, Professional, Depression, Health Personnel, Humans, Mental Health, Risk Factors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
