As much as 20.3% of college students experience mental health disorders, such as anxiety, depression or substance abuse disorder. Despite this, only 16.4% of students with mental health problems received any form of treatment (Auerbach et al., 2016). Other studies show college students suffer from mental health disorders with numbers as high as 31% (Mortier et al., 2018). Mental health issues like depression and anxiety significantly impair a student’s ability to focus and retain information, which leads to missing classes, failing assignments and higher rates of academic attrition (Eisenberg et al., 2009; Storrie et al., 2010).
The demand for mental health care practitioners is almost constantly increasing, while time, money, and effort are limited. The ability of AI to help accommodate this high demand has made many people hopeful to receive the mental health care they urgently need. However, there are also drawbacks to replacing real doctors with a robot. One of the main problems is the dehumanization of healthcare; a field traditionally known for its compassionate care. I also noticed that the human dimension of healthcare is invaluable and hard to replace with AI.
Even though the therapist-patient relationship is hard to emulate with the use of AI, AI can still prove beneficial for anyone struggling with mental health issues. Artificial Intelligence can, for example, access relevant patient-related data from various sources and through triangulation come up with an accurate assessment of someone’s mental health status (Walsh et al., 2017). AI is superior to a human when it comes to uncovering patterns in seemingly unrelated datasets.
In conclusion I would say that AI certainly has very promising features to aid mental health practitioners, but it also has very clear shortcomings. I think for now the best course of action in the field of healthcare is to combine the care of a therapist with the insights of AI.
Auerbach, R. P., Alonso, J., Axinn, W. G., Cuijpers, P., Ebert, D. D., Green, J. G., Hwang, I., Kessler, R. C., Liu, H., Mortier, P., Nock, M. K., Pinder-Amaker, S., Sampson, N. A., Aguilar-Gaxiola, S., Al-Hamzawi, A., Andrade, L. H., Benjet, C., Caldas-De-Almeida, J. M., Demyttenaere, K., . . . Bruffaerts, R. (2016). Mental disorders among college students in the World Health Organization World Mental Health Surveys. Psychological Medicine, 46(14), 2955–2970. https://doi.org/10.1017/s0033291716001665
Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., Demyttenaere, K., Ebert, D. D., Green, J. G., Hasking, P., Murray, E., Nock, M. K., Pinder-Amaker, S., Sampson, N. A., Stein, D. J., Vilagut, G., Zaslavsky, A. M., & Kessler, R. C. (2018). WHO World Mental Health Surveys International College Student Project: Prevalence and distribution of mental disorders. Journal of Abnormal Psychology, 127(7), 623–638. https://doi.org/10.1037/abn0000362
Eisenberg, D., Golberstein, E., & Hunt, J. B. (2009). Mental health and academic success in college. The B E Journal of Economic Analysis & Policy, 9(1). https://doi.org/10.2202/1935-1682.2191
Storrie, K., Ahern, K., & Tuckett, A. (2010). A systematic review: Students with mental health problems—A growing problem. International Journal of Nursing Practice, 16(1), 1–6. https://doi.org/10.1111/j.1440-172x.2009.01813.x
Walsh CG, Ribeiro JD, Franklin JC (2017) Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci 5:457–469
