AI for Mental Health: Improving Diagnosis, Treatment, and Support

References

[1] WHO. (2022). Mental health. World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/mental-health

[2] Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial Intelligence in Healthcare: Past, Present, and Future. Stroke and Vascular Neurology, 2(4), 230-243.

[3] Denecke, K., Bamidis, P., Bond, C., et al. (2021). Ethical Challenges of Artificial Intelligence in Medicine and Healthcare. Health Informatics Journal, 27(3), 146045822097420.

[4] Inkster, B., Sarda, S., & Subramanian, V. (2018). An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation. JMIR mHealth and uHealth, 6(11), e12106.

[5] Shatte, A. B., 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.

[6] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

[7] Shatte, A. B., 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.

[8] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.

[9] Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649-685.

[10] Vieira, S., Pinaya, W. H. L., & Mechelli, A. (2017). Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74, 58-75.

[11] Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), 15.

[12] Arbabshirani, M. R., Plis, S. M., Sui, J., Calhoun, V. D., & Silva, R. F. (2017). Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage, 145, 137-165.

[13] Fraschini, M., Demuru, M., Crobe, A., Marrosu, F., Stam, C. J., & Hillebrand, A. (2016). The effect of epoch length on estimated EEG functional connectivity and brain network organization. Journal of Neural Engineering, 13(3), 036015.

[14] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

[15] Shatte, A. B., 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.

[16] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.

[17] Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Mental Health, 4(2), e19.

[18] Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106.

[19] Andersson, G., Carlbring, P., & Titov, N. (2019). Internet interventions for adults with anxiety and mood disorders: A narrative umbrella review of recent meta-analyses. Canadian Journal of Psychiatry, 64(7), 465-470.

[20] 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.

[21] Miller, D. J., & Brown, W. J. (2017). Mental health and well-being through physical activity: The role of physical activity in preventing and treating mental health disorders. Current Sports Medicine Reports, 16(4), 265-271.

[22] Torous, J., & Roberts, L. W. (2017). The ethical use of mobile health technology in clinical psychiatry. Journal of Nervous and Mental Disease, 205(1), 4-8.

[23] De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13, 1-10.

[24] Luxton, D. D., June, J. D., & Fairall, J. M. (2012). Social media and suicide: a public health perspective. American Journal of Public Health, 102(S2), S195-S200.

[25] Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019). A taxonomy of ethical tensions in inferring mental health states from social media. Proceedings of the Conference on Fairness, Accountability, and Transparency, 79-88.

[26] Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L. H., & Polsky, D. E. (2019). Studying expressions of loneliness in individuals using twitter: an observational study. BMJ Open, 9(11), e030355.

[27] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

[28] Riva, G., Wiederhold, B. K., & Mantovani, F. (2019). Neuroscience of virtual reality: from virtual exposure to embodied medicine. Cyberpsychology, Behavior, and Social Networking, 22(1), 82-96.

[29] Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43.

[30] Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36-43.

[31] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

[32] Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.

[33] Kleinman, A. (2004). Culture and depression. New England Journal of Medicine, 351(10), 951-953.

[34] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

@article{1706334,
      author = {Samson Oseiwe Ajadalu, Rockson Amoah-Saah, Joseph Chibueze Orsuamaeze, Chidi Godwin Nweke, Bukola Eunice Adegboye},
      title = {AI for Mental Health: Improving Diagnosis, Treatment, and Support},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {3},
      pages = {461-468},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706334.pdf},
      abstract = {Mental health disorders represent a significant global public health challenge, affecting millions annually and contributing substantially to global disability. Despite the prevalence of these conditions, many individuals do not receive adequate care due to barriers such as stigma, lack of resources, and insufficient access to qualified professionals. This article explores the transformative potential of Artificial Intelligence (AI) in mental health care, focusing on its applications in diagnosis, treatment, and ongoing support. By leveraging advanced algorithms, machine learning, and natural language processing, AI can enhance the accuracy and efficiency of mental health diagnoses, personalize treatment plans, and provide continuous support through digital platforms. The article also addresses the technical, clinical, and societal challenges associated with integrating AI into mental health care and emphasizes the importance of interdisciplinary collaboration and ethical considerations. The goal is to inform stakeholders, including healthcare professionals, policymakers, and researchers, about the opportunities AI presents and advocate for its responsible and ethical use in mental health.},
      keywords = {Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), Mental Health, Diagnosis.},
      month = {September},
  }