Review of Artificial Intelligence Applications in Mental Health Diagnosis and Therapy
DOI:
https://doi.org/10.59388/josc.v4i2.957Keywords:
Artificial Intelligence, Diagnosis, Treatment, Mental Health, Narrative Literature ReviewAbstract
Mental illness represents a growing global health concern, with increasing prevalence and burden across populations. Timely diagnosis and effective treatment are essential to improving individual health outcomes. In this context, artificial intelligence (AI) offers significant potential through rapid and precise data analysis. This narrative literature review aims to examine the application of AI technologies, specifically natural language processing (NLP), machine learning (ML), and predictive modelling, in the diagnosis and therapy of mental health disorders among adult clinical populations. The review includes twelve peer-reviewed articles indexed in Scopus between 2014 and 2024, selected based on relevance, methodological rigor, and contribution to the field. Findings were organised into three thematic clusters: (1) AI-assisted diagnostic accuracy and predictive modelling (e.g., fMRI-based PTSD prediction, depression detection via multimodal neural networks); (2) AI-enhanced therapeutic delivery and user engagement (e.g., AI-assisted online social therapy, remotely supervised brain stimulation); and (3) ethical, privacy, and implementation challenges (e.g., data bias, lack of transparency, and population representativeness). These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets. Future research should focus on developing standardised implementation protocols using frameworks such as PRISMA, ensuring population diversity, and addressing ethical safeguards. Collaboration among mental health professionals, AI technologists, and policymakers is essential to promote safe, effective, and equitable integration of AI in psychological diagnosis and therapy.
References
Abd-Alrazaq, A., Alhuwail, D., Schneider, J., Toro, C. T., Ahmed, A., Alzubaidi, M., Alajlani, M., & Househ, M. (2022). The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review. npj Digital Medicine, 5, Article 87. https://doi.org/10.1038/s41746-022-00631-8
Banerjee, S., Lio, P., Jones, P. B., & Cardinal, R. N. (2021). A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness. NPJ Schizophrenia, 7, Article 61. https://doi.org/10.1038/s41537-021-00181-0
Balcombe, L. (2023). AI chatbots in digital mental health. Informatics, 10(4), Article 82. https://doi.org/10.3390/informatics10040082
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cao, Y., Chen, R. C., & Katz, A. J. (2024). Why is a small sample size not enough? The Oncologist, 29(9), 761–763. https://doi.org/10.1093/oncolo/oyae105
Carbonell, Á., Navarro-Pérez, J. J., & Mestre, M. V. (2020). Challenges and barriers in mental healthcare systems and their impact on the family: A systematic integrative review. Health & Social Care in the Community, 28(5), 1366–1379. https://doi.org/10.1111/hsc.12968
Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: A critical review. NPJ Digital Medicine, 3, Article 43. https://doi.org/10.1038/s41746-020-0233-7
Clark, I. A., Niehaus, K. E., Duff, E. P., Di Simplicio, M. C., Clifford, G. D., Smith, S. M., Mackay, C. E., Woolrich, M. W., & Holmes, E. A. (2014). First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage. Behaviour Research and Therapy, 62, 37–46. https://doi.org/10.1016/j.brat.2014.06.006
Ćosić, K., Popović, S., Šarlija, M., Kesedžić, I., Gambiraža, M., Dropuljić, B., Mijić, I., Henigsberg, N., & Jovanovic, T. (2021). AI-based prediction and prevention of psychological and behavioral changes in ex-COVID-19 patients. Frontiers in Psychology, 12, Article 636228. https://doi.org/10.3389/fpsyg.2021.636228
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
D'Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., Gleeson, J., & Alvarez-Jimenez, M. (2017). Artificial intelligence-assisted online social therapy for youth mental health. Frontiers in Psychology, 8, Article 796. https://doi.org/10.3389/fpsyg.2017.00796
Dalgleish, T., Black, M., Johnston, D., & Bevan, A. (2020). Transdiagnostic approaches to mental health problems: Current status and future directions. Journal of Consulting and Clinical Psychology, 88(3), 179–195. https://doi.org/10.1037/ccp0000482
Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M. (2023). ChatGPT outperforms humans in emotional awareness evaluations. Frontiers in Psychology, 14, Article 1199058. https://doi.org/10.3389/fpsyg.2023.1199058
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF
Fusar-Poli, P., Correll, C. U., Arango, C., Kelleher, I., Treasure, J., & Kessing, L. V. (2021). Preventive psychiatry: A blueprint for improving the mental health of young people. World Psychiatry, 20(2), 200–221. https://doi.org/10.1002/wps.20869
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., Narayanan, S. S., & Atkins, D. C. (2020). Machine learning and natural language processing in psychotherapy research: Alliance as example use case. Journal of Counseling Psychology, 67(4), 438–448. https://doi.org/10.1037/cou0000382
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49. https://doi.org/10.1016/j.cobeha.2017.07.005
Kilbourne, A. M., Beck, K., Spaeth-Rublee, B., Ramanuj, P., O’Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018). Measuring and improving the quality of mental health care: A global perspective. World Psychiatry, 17(1), 30–38. https://doi.org/10.1002/wps.20482
Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., Schmidt, A., Gillam, J., Schultze-Lutter, F., Falkai, P., Reiser, M., Riecher-Rössler, A., Upthegrove, R., Hietala, J., Salokangas, R. K. R., Pantelis, C., … Schroeder, J. (2018). Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: A multimodal, multisite machine learning analysis. JAMA Psychiatry, 75(11), 1156–1172. https://doi.org/10.1001/jamapsychiatry.2018.2165
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. BMJ, 339, Article b2700. https://doi.org/10.1136/bmj.b2700
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), Article e1000097. https://doi.org/10.1371/journal.pmed.1000097
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017). Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 13, 23–47. https://doi.org/10.1146/annurev-clinpsy-032816-044949
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Paneva, J., Leunissen, I., Schuhmann, T., de Graaf, T. A., Jønsson, M. G., Onarheim, B., & Sack, A. T. (2022). Using remotely supervised at-home transcranial electrical stimulation (TES) for enhancing mental resilience. Frontiers in Human Neuroscience, 16, Article 838187. https://doi.org/10.3389/fnhum.2022.838187
Rahman, M. M. (2023). AI for ADHD: Opportunities and challenges. Journal of Attention Disorders, 27(8), 797–799. https://doi.org/10.1177/10870547231160504
Richmond, S., Bell, M., Ngo, D., & Yap, M. B. H. (2024). A non-randomized feasibility study of a voice assistant for parents to support their children's mental health. Frontiers in Psychology, 15, Article 1390556. https://doi.org/10.3389/fpsyg.2024.1390556
Ritchie, H., & Roser, M. (2018). Mental health. Our World in Data. https://ourworldindata.org/mental-health
Saeidnia, H. R., Hashemi Fotami, S. G., Lund, B., & Ghiasi, N. (2024). Ethical considerations in artificial intelligence interventions for mental health and well-being: Ensuring responsible implementation and impact. Social Sciences, 13(7), Article 381. https://doi.org/10.3390/socsci13070381
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Thieme, A., Belgrave, D., & 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), Article 34. https://doi.org/10.1145/3398069
Torous, J., Kiang, M. V., Lorme, J., & Onnela, J.-P. (2016). New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research. JMIR Mental Health, 3(2), e16. https://doi.org/10.2196/mental.5165
Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), Article e1002689. https://doi.org/10.1371/journal.pmed.1002689
Victor, E., Aghajan, Z. M., Sewart, A. R., & Christian, R. (2019). Detecting depression using a framework combining deep multimodal neural networks with a purpose-built automated evaluation. Psychological Assessment, 31(8), 1019–1031. https://doi.org/10.1037/pas0000822
Wasil, A. R., Gillespie, S., Patel, R., Petre, A., Venturo-Conerly, K. E., Shingleton, R. M., Weisz, J. R., & DeRubeis, R. J. (2020). Reassessing evidence-based content in popular smartphone apps for depression and anxiety: Developing and applying user-adjusted analyses. Journal of Consulting and Clinical Psychology, 88(11), 983–993. https://doi.org/10.1037/ccp0000492
Weaver, A., Zhang, A., Xiang, X., Felsman, P., Fischer, D. J., & Himle, J. A. (2023). Entertain me well: An entertaining, tailorable, online platform delivering CBT for depression. Cognitive and Behavioral Practice, 30(1), 1–14. https://doi.org/10.1016/j.cbpra.2021.10.004
Williams, D. R. (2022). Stress and the mental health of populations of color: Advancing our understanding of race-related stressors. Journal of Health and Social Behavior, 63(1), 3–19. https://doi.org/10.1177/00221465211073027
World Health Organization. (2020). Mental health: Strengthening our response. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response.
World Health Organization. (2022). Mental health and COVID-19: Early evidence of the pandemic's impact. https://www.who.int/publications/i/item/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1
