Mental Health and Artificial Intelligence (AI): Marching towards Future

Authors

  • Boominathan V
  • Roopamathy M

Abstract

One of the most crucial but frequently disregarded facets of our wellbeing is our mental health. In the medical domains of dermatology, radiology, and oncology, artificial intelligence (AI) is being used more and more. AI hasn't been heavily utilised in mental healthcare, though. The need for AI to help identify high-risk individuals and give interventions to prevent and treat mental illnesses is essential due to the high morbidity and mortality rates associated with psychiatric disorders and the growing lack of mental healthcare providers. Although there isn't much published research on AI in psychiatry, there are increasing numbers of successful applications of AI in social media platforms, brain imaging, electronic health records, and sensor-based monitoring systems that can be used to predict, categories, or subgroup mental illnesses as well as issues like suicidality. With the help of this technology, we can now think differently about mental health and give individuals who are battling fresh hope. We will examine the use of AI, its difficulties, and potential issues in the field of mental health in this article.

References

Artificial Intelligence: Oxford English Dictionary. Last accessed on 2023 November 29 Available from: https://www.oed.com/viewdictionaryentry/Entry/271625.

Gold E. The History of Artificial Intelligence from the 1950s: https://www.freecodecamp.org/news/the-history-of-ai/.

Zhang P, Kumar N, Zakarya M, Abualigah L. Editorial: Artificial intelligence for mental disorder prevention and diagnosis: Technologies and challenges Front Psychiatry. 2023;14:1–2

Abd-Alrazaq A, Alhuwail D, Schneider J, Toro CT, Ahmed A, Alzubaidi M, et al The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review NPJ Digit Med. 2022;5:87

Zhang W, Yang C, Cao Z, Li Z, Zhuo L, Tan Y, et al Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging EBioMedicine. 2023;90:104541

Li X. Evaluation and analysis of elderly mental health based on artificial intelligence Occup Ther Int. 2023;2023:1–11

Ahmed A, Aziz S, Toro CT, Alzubaidi M, Irshaidat S, Serhan HA, et al Machine learning models to detect anxiety and depression through social media: A scoping review Comput Methods Programs Biomed Update. 2022;2:100066

Rotenberg LS, Borges-Júnior RG, Lafer B, Salvini R, Dias RD. Exploring machine learning to predict depressive relapses of bipolar disorder patients J Affect Disord. 2021;295:681–7

Darcy A, Beaudette A, Chiauzzi E, Daniels J, Goodwin K, Mariano TY, et al Anatomy of a Woebot® (WB001): Agent guided CBT for women with postpartum depression Expert Rev Med Devices. 2022;19:287–301

Mathur A, Munshi H, Varma S. Effectiveness of Artificial intelligence in cognitive behavioral therapy ICT with Intelligent Applications (Smart Innovation, Systems and Technologies). 2022;248 Singapore Springer Singapore:413–23 Available from: https://link.springer.com/10.1007/978-981-16-4177-0_42. [Last accessed on 2023 May 29]

Sinha C, Meheli S, Kadaba M. Understanding digital mental health needs and usage with an artificial intelligence-led mental health App (Wysa) during the COVID-19 pandemic: Retrospective analysis JMIR Form Res. 2023;7:e41913

Wiebe A, Kannen K, Selaskowski B, Mehren A, Thöne AK, Pramme L, et al Virtual reality in the diagnostic and therapy for mental disorders: A systematic review Clin Psychol Rev. 2022;98:102213

Yang S, Liu K, Gai J, He X. Transformation to industrial artificial intelligence and workers' mental health: Evidence from China Front Public Health. 2022;10:881827

Wei W, Li L. The impact of artificial intelligence on the mental health of manufacturing workers: The mediating role of overtime work and the work environment Front Public Health. 2022;10:862407

Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review Laryngoscope Investig Otolaryngol. 2020;5:96–116

Yan WJ, Ruan QN, Jiang K. Challenges for artificial intelligence in recognizing mental disorders Diagnostics (Basel). 2022;13:2

Joyce DW, Kormilitzin A, Smith KA, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability NPJ Digit Med. 2023;6:6

Timmons AC, Duong JB, Simo Fiallo N, Lee T, Vo HP, Ahle MW, et al A call to action on assessing and mitigating bias in artificial intelligence applications for mental health Perspect Psychol Sci. 2022 17456916221134490

Singh OP. Artificial intelligence in the era of ChatGPT - Opportunities and challenges in mental health care Indian J Psychiatry. 2023;65:297–8

Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and quality flaws in the use of artificial intelligence in mental health research: Systematic review JMIR Ment Health. 2023;10:e42045

Sharma A, Lin IW, Miner AS, Atkins DC, Althoff T. Towards facilitating empathic conversations in online mental health support: a reinforcement learning approach. In: Proceedings of the Web Conference 2021. Ljubljana: ACM (2021). p. 194–205. doi: 10.1145/3442381.3450097

Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H-C, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep. (2019) 21:116. doi: 10.1007/s11920-019-1094-0

Graham SA, Lee EE, Jeste DV, Van Patten R, Twamley EW, Nebeker C, et al. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: a conceptual review. Psychiatry Res. (2020) 284:112732. doi: 10.1016/j.psychres.2019.112732

Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim H-C, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging. (2021) 6:856–64. doi: 10.1016/j.bpsc.2021.02.001

Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, et al. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry. (2021) 20:154–70. doi: 10.1002/wps.20882

Unützer J. Late-life depression. N Engl J Med. (2007) 357:2269–76. doi: 10.1056/NEJMcp073754

Folsom DP, Lindamer L, Montross LP, Hawthorne W, Golshan S, Hough R, et al. Diagnostic variability for schizophrenia and major depression in a large public mental health care system dataset. Psychiatry Res. (2006) 144:167–75. doi: 10.1016/j.psychres.2005.12.002

Mohr DC, Zhang M, Schueller SM. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol. (2017) 13:23–47. doi: 10.1146/annurev-clinpsy-032816-044949

Berke EM, Choudhury T, Ali S, Rabbi M. Objective measurement of sociability and activity: mobile sensing in the community. Ann Fam Med. (2011) 9:344–50. doi: 10.1370/afm.1266

Taler V, Phillips NA. Language performance in Alzheimer's disease and mild cognitive impairment: a comparative review. J Clin Exp Neuropsychol. (2008) 30:501–56. doi: 10.1080/13803390701550128

Beltrami D, Gagliardi G, Rossini Favretti R, Ghidoni E, Tamburini F, Calzà L. Speech analysis by natural language processing techniques: a possible tool for very early detection of cognitive decline? Front Aging Neurosci. (2018) 10:369. doi: 10.3389/fnagi.2018.00369

Gil D, Johnsson M. Diagnosing Parkinson by using artificial neural networks and support vector machines. Glob J Comput Sci Technol. (2009) 9:63–71.

Brenes GA, Danhauer SC, Lyles MF, Hogan PE, Miller ME. Barriers to mental health treatment in rural older adults. Am J Geriatr Psychiatry. (2015) 23:1172–8. doi: 10.1016/j.jagp.2015.06.002

Jimenez DE, Bartels SJ, Cardenas V, Dhaliwal SS, Alegría M. Cultural beliefs and mental health treatment preferences of ethnically diverse older adult consumers in primary care. Am J Geriatr Psychiatry. (2012) 20:533–42. doi: 10.1097/JGP.0b013e318227f876

Mojtabai R, Olfson M, Sampson NA, Jin R, Druss B, Wang PS, et al. Barriers to mental health treatment: results from the National Comorbidity Survey Replication. Psychol Med. (2011) 41:1751–61. doi: 10.1017/S0033291710002291

Areán PA, Renn BN, Ratzliff A. Making psychotherapy available in the United States: implementation challenges and solutions. Psychiatr Serv. (2021) 72:222–4. doi: 10.1176/appi.ps.202000220

Downloads

Published

2023-12-29

Issue

Vol. 13 No. 3 (2023)

Section

Review Article

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.