ARTIFICIAL INTELLIGENCE IN MENTAL HEALTHCARE
EOI: 10.11242/viva-tech.01.06.012
Citation
Prof. Shreya Bhamare, Onkar Shelar, Siddhesh Rane, "ARTIFICIAL INTELLIGENCE IN MENTAL HEALTHCARE", VIVA-IJRI Volume 1, Issue 6, Article MCA_12, pp. 1-6, 2023. Published by MCA Department, VIVA Institute of Technology, Virar, India.
Abstract
A summary of recent original research on AI specifically related to mental health is provided in this article, along with an overview of AI and its present uses in mental healthcare. The development of prediction, diagnosis, and treatment options for mental health care is being aided by current artificial intelligence (AI), and machine learning in particular. To improve user experience and optimise individualised mental health care, AI is being applied into digital treatments, particularly online and smartphone apps. To create prediction/detection models for mental health disorders, AI techniques can be used. In order to assist with clinical diagnosis, prognosis, and therapy as well as clinical and technological challenges, this article presents an overview of AI techniques in mental healthcare.
Keywords
Depression, Machine Learning, Natural Language Processing, Stress, Suicide.
References
- Aggarwal, N., & Chandra, M. (2020). Applications of artificial intelligence in mental health: opportunities and limitations. Indian Journal of Psychiatry, 62(3), 222–227. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_682_19
- Burgess, M. (2018, June 15). The NHS is trialling an AI chatbot to answer your medical questions. Wired UK. https://www.wired.co.uk/article/babylon-nhs-chatbot-app
- Smith, J. D. (2021, January 15). The role of artificial intelligence in mental health care. Psychology Today. https://www.psychologytoday.com/us/blog/mind-machine/202101/the-role-artificial-intelligence-in-mental-health-care
- Butler, A. C., Chapman, J. E., Forman, E. M., & Beck, A. T. (2006). The empirical status of cognitive-behavioral therapy: A review of meta-analyses. Clinical Psychology Review, 26(1), 17-31.
- 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(11), 116. https://doi.org/10.1007/s11920-019-1087-3.
- Gaffney, H., Mansell, W., & Tai, S. (2021). Conversational agents and their potential role in the delivery of psychological interventions for mental health: A systematic review. Evidence-Based Mental Health, 24(1), 22-27. https://doi.org/10.1136/ebmental-2020-300203
- Andersson, G., & Cuijpers, P. (2009). Internet-based and other computerized psychological treatments for adult depression: A meta-analysis. Cognitive Behaviour Therapy, 38(4), 196-205. https://doi.org/10.1080/16506070903318960
- Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., Mitchell, M., & Sanghvi, S. (2018). CLPsych 2018 shared task: Predicting social media postpartum depression with machine learning and human interpretation. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic (pp. 170-179). https://doi.org/10.18653/v1/W18-0518
- National Institutes of Health (NIH). (2019). What is precision medicine? Retrieved from https://ghr.nlm.nih.gov/primer/precisionmedicine/definition
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
- Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
- Wenqian Chen1, Vikram Karde1, Thomas N. H. Cheng1, Siti S. Ramli2 , Jerry Y. Y. Heng1, “Surface hydrophobicity: effect of alkyl chain length and network homogeneity”
- Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.
- Daim, T. U., Rueda, G., & Martin, H. (2021). Artificial Intelligence in Medicine. In Artificial Intelligence in Practice for Business (pp. 263-293). Springer, Cham. https://doi.org/10.1007/978-3-030-65817-5_11
- "AI Index 2018 Annual Report." AI Index, Stanford University, 2018, https://aiindex.org/2018-report/.
- Kim, Y., Chen, Y., & Kumar, M. (2019). Big data analytics in healthcare: A systematic literature review, taxonomy, and future research directions. Journal of biomedical informatics, 103253.