Abdul Rahman, H., Kwicklis, M., Ottom, M., Amornsriwatanakul, A., K HA-M., Rosenberg, M., & Dinov, I. D. (2023). Machine learning-based prediction of mental well-being using health behavior data from university students. Bioengineering, 10(5).
https://doi.org/10.3390/bioengineering10050575
Adams, D., & Thompson, P. (2025). Transforming school leadership with artificial intelligence: Applications, implications, and future directions. Leadership and Policy in Schools, 24(1), 77–89.
https://doi.org/10.1080/15700763.2024.2411295
Aggarwal, A., Tam, C. C., Wu, D., Li, X., & Qiao, S. (2023). Artificial intelligence–based chatbots for promoting health behavioral changes: Systematic review. Journal of Medical Internet Research, 25, e40789.
doi:10.2196/40789
Aleem, S., Huda, N. U., Amin, R., Khalid, S., Alshamrani, S. S., & Alshehri, A. (2022). Machine learning algorithms for depression: Diagnosis, insights, and research directions. Electronics, 11(7), 1111.
https://doi.org/10.3390/electronics11071111
Alkahtani, H., Aldhyani, T. H., & Alqarni, A. A. (2024). Artificial intelligence models to predict disability for mental health disorders. Journal of Disability Research, 3(3), 20240022.
Anbarasi, L. J., Jawahar, M., Ravi, V., Cherian, S. M., Shreenidhi, S., & Sharen, H. (2022). Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown. Health Technology (Berlin), 12(4), 825–838.
https://doi.org/10.1007/s12553-022-00674-7.
Atlam, E.-S., Rokaya, M., Masud, M., Meshref, H., Alotaibi, R., Almars, A. M., Assiri, M., & Gad, I. (2025). Explainable artificial intelligence systems for predicting mental health problems in autistics. Alexandria Engineering Journal, 117, 376–390.
https://doi.org/10.1016/j.aej.2024.12.120
Baghdadi, N. A., Farghaly Abdelaliem, S. M., Malki, A., Gad, I., Ewis, A., & Atlam, E. (2023). Advanced machine learning techniques for cardiovascular disease early detection and diagnosis. Journal of Big Data, 10(1), 144.
https://doi.org/10.1186/s40537-023-00817-1.
Baba, A., & Bunji, K. (2023). Prediction of mental health problem using annual student health survey: Machine learning approach. JMIR Mental Health, 10, e42420. doi:
10.2196/42420.
Cui, Y., Shi, X., Qin, Y., Wan, Q., Cao, X., Che, X., Pan, Y., Wang, B., Lei, M., & Liu, Y. (2024). Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: A multicenter analysis. International Journal of Surgery. 10.1097/JS9.0000000000001169.
Dehbozorgi, R., Zangeneh, S., Khooshab, E., Hafezi Nia, D., Hanif, H. R., Samian, P., … & Lohrasebi, F. (2025). The application of artificial intelligence in the field of mental health: A systematic review. BMC Psychiatry, 25, 132.
https://doi.org/10.1186/s12888-025-06483-2. [In Persian]
Delello, J. A., Sung, W., Mokhtari, K., Hebert, J., Bronson, A., & De Giuseppe, T. (2025). AI in the classroom: Insights from educators on usage, challenges, and mental health. Education Sciences, 15(2), 113.
https://doi.org/10.3390/educsci15020113
Dobson, K. S., & Dozois, D. J. (2021). Handbook of cognitive-behavioral therapies. Guilford Publications.
Dogan, M., & Arslan, H. (2025). The role of artificial intelligence in school leadership. Revista de Pedagogie Digitala, 4(1), 23–30. ID: 95424e176bb39dda.
Fallah, N., Karimi, S., Moqerhasani, R., & Hosseini Mojared Hosseinabadi, F. S. (2014). Application of artificial intelligence in job design and job stress and mental health among employees of an organization. Sixth International Conference and Ninth National Conference on Management, Psychology and Behavioral Sciences, Tehran.
https://civilica.com/doc/2156053. [In Persian]
Filip, S., Dubrovina, N., & Sidak, M. (2023). Organization and financing of healthcare in the Slovak Republic and selected European countries. In N. Kryvinska, M. Greguš, & S. Fedushko (Eds.), Developments in information and knowledge management systems for business applications (Vol. 466). Springer.
https://doi.org/10.1007/978-3-031-27506-7_10.
Gad, M., Elmezain, M. M., Alwateer, M., Almaliki, M., Elmarhomy, G., & Atlam, E. (2023). Breast cancer diagnosis using a machine learning model and swarm intelligence approach. In 2023 1
st International Conference on Advanced Innovations in Smart Cities (ICAISC) (pp. 1–5).
https://doi.org/10.1109/icaisc56366.2023.10085393
Goodman, C., et al. (1988). American Medical Association Council on Scientific Affairs. In Medical technology assessment directory: A pilot reference to organizations, assessments, and information resources. National Academies Press (US). DOI: 10.17226/1090
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21, 1–18. Doi: 10.1007/s11920-019-1094-0.
Hamilton, M. (1967). Development of a rating scale for primary depressive illness. British Journal of Social and Clinical Psychology, 6(4), 278–296. DOI: 10.1111/j.2044-8260.1967.tb00530.x.
Han, T., Xiong, F., Sun, B., Zhong, L., Han, Z., & Lei, M. (2024). Development and validation of an artificial intelligence mobile application for predicting 30-day mortality in critically ill patients with orthopaedic trauma. International Journal of Medical Informatics, 184, 105383. Doi: 10.1016/j.ijmedinf.2024.105383.
Hoose, S., & Králiková, K. (2024). Artificial intelligence in mental health care: Management implications, ethical challenges, and policy considerations.
Administrative Sciences, 14, 227.
https://doi.org/10.3390/admsci14090227
Hooshmand, M. K., Huchaiah, M. D., Alzighaibi, A. R., Hashim, H., Atlam, E. S., & Gad, I. (2024). Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI).
Alexandria Engineering Journal, 94, 120–130. DOI:
10.1016/j.aej.2024.03.041
Kakuma, R., Minas, H., Van Ginneken, N., Dal Poz, M. R., Desiraju, K., Morris, J. E., ... & Scheffler, R. M. (2011). Human resources for mental health care: Current situation and strategies for action. The Lancet, 378(9803), 1654–1663.doi: 10.1016/S0140-6736(11)61093-3.
Kakungulu, S. J. (2025). The impact of artificial intelligence on educational administration. Eurasian Experiment Journal of Arts and Management (EEJAM), 7(1).ISSN: 2992-412X
Le Glaz, A., Haralambous, Y., Kim-Dufor, D.-H., Lenca, P., Billot, R., Ryan, T. C., ... & Berrouiguet, S. (2021). Machine learning and natural language processing in mental health: Systematic review. Journal of Medical Internet Research, 23(5), e15708. doi:10.2196/15708
Lei, M., Wu, B., Zhang, Z., Qin, Y., Cao, X., Cao, Y., ... & Liu, Y. (2023). A web-based calculator to predict early death among patients with bone metastasis using machine learning techniques: Development and validation study. Journal of Medical Internet Research, 25, e47590.doi: 10.2196/47590.
Malki, A., Atlam, E.-S., Hassanien, A. E., Ewis, A., Dagnew, G., & Gad, I. (2022). SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines.
Alexandria Engineering Journal, 61(12), 12091–12110.
https://doi.org/10.1016/j.aej.2022.05.051.
Meda, N., Pardini, S., Rigobello, P., Visioli, F., & Novara, C. (2023). Frequency and machine learning predictors of severe depressive symptoms and suicidal ideation among university students. Epidemiology and Psychiatric Sciences, 32, e42.doi: 10.1017/S2045796023000550
Noor, T. H., Almars, A., Gad, I., Atlam, E. S., & Elmezain, M. (2022). Spatial impressions monitoring during COVID-19 pandemic using machine learning techniques. Computers, 11(4), 52.https://doi.org/10.3390/computers11040052
Noori, R. Khastar, H. Yeganehfard, K. & Razeghi, A. (2024). The Impact of Artificial Intelligence on Employee Health and Safety in the Workplace. Intelligent Management of Human Capital, 1 (2), 1-28. DOI: https://doi.org/10.22034/imhr.2025.479050.1010. [In Persian]
Oladimeji, K. E., Nyatela, A., Gumede, S., Dwarka, D., & Lalla-Edward, S. T. (2023). Impact of artificial intelligence (AI) on psychological and mental health promotion: An opinion piece. New Voices in Psychology, 13, 12.DOI: https://doi.org/10.25159/2958-3918/14548
Oyeronke, C. P. (2025). The impact of artificial intelligence on educational leadership: Theoretical frameworks for measurement and evaluation. Jurnal Saintifik Multi Science Journal, 23(1), 47–72.DOI: https://doi.org/10.58222/js.v23i1.389
Raisi-Gehroui, H. (2024). Artificial intelligence and its application in psychology and psychiatry. DOI:10.32598/ajnpp.4.3.210[In Persian]
Ratul, I. J., Nishat, M. M., Faisal, F., Sultana, S., Ahmed, A., & Al Mamun, M. A. (2023). Analyzing perceived psychological and social stress of university students: A machine learning approach. Heliyon, 9(6), e17307.doi: 10.1016/j.heliyon.2023.e17307.
Rois, R., Ray, M., Rahman, A., & Roy, S. K. (2021). Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms. Journal of Health, Population and Nutrition, 40(1), 50.https://doi.org/10.1186/s41043-021-00276-5
Shi, X., Cui, Y., Wang, S., Pan, Y., Wang, B., & Lei, M. (2024). Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques. The Spine Journal, 24(1), 146–160.https://doi.org/10.1016/j.spinee.2023.09.001
Shah, V. (2022). AI in mental health: Predictive analytics and intervention strategies. Free ChatGPT Extension.https://doi.org/10.5281/zenodo.10779085.
Sposato, M. (2025). Artificial intelligence in educational leadership: A comprehensive taxonomy and future directions.
International Journal of Educational Technology in Higher Education.
https://doi.org/10.1186/s41239-025-00517-1
Srebalová, M., Vojtech, F., Pekár, B., Mikušová-Mericková, B., & Horvát, M. (2018). Restriction on the re-export of medicinal products and the supervision of compliance with it by public administration bodies. European Pharmaceutical Journal, 65, 24–30.DOI: https://doi.org/10.1515/afpuc-2017-0009
Tache, P. C. E., & Sararu, C. S. (2023). New transdisciplinary directions in international law? Lex Humana, 15, 86–109.https://seer.ucp.br/seer/index.php/LexHumana/article/view/2705.
Thakkar, A., Gupta, A., & De Sousa, A. (2024). Artificial intelligence in positive mental health: A narrative review.
Frontiers in Digital Health, 6, 1280235.
https://doi.org/10.3389/fdgth.2024.1280235
Thieme, D., Belgrave, M., & Doherty, G. (2020). Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction, 27(5), 1–53.https://doi.org/10.1145/3398069
Vitriol, V., Cancino, A., Weil, K., Salgado, C., Asenjo, M. A., Potthoff, S., et al. (2014). Depression and psychological trauma: An overview integrating current research and specific evidence of studies in the treatment of depression in public mental health services in Chile. Depression Research and Treatment, https://doi.org/10.1145/3398069.
Xu, L., Sanders, L., Li, K., & Chow, J. C. L. (2021). Chatbot for health care and oncology applications using artificial intelligence and machine learning (Preprint). JMIR Cancer, 7, e27850.doi:10.2196/27850
Zhang, L., Zhao, S., Yang, Z., Zheng, H., & Lei, M. (2024). An artificial intelligence platform to stratify the risk of experiencing sleep disturbance in university students after analyzing psychological health, lifestyle, and sports: A multicenter externally validated study.
Psychology Research and Behavior Management, 17, 1057–1071.DOI
https://doi.org/10.2147/PRBM.S448698.