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