Abstract
Scientific findings over the past few decades have shaped our understanding of the underlying neurobiology associated with psychiatric illnesses. However, despite significant advances in research, there is widespread disappointment with the overall pace of progress in detecting and treating psychiatric disorders. Current approaches for the diagnosis of psychiatric disorders largely rely on physician-patient questionnaires that are most of the time inaccurate and ineffective in providing a reliable assessment of symptoms. These limitations can, however, be overcome by applying artificial intelligence (AI) to electronic medical database and health records. AI in psychiatry is a general term that implies the use of computerized techniques and algorithms for the diagnosis, prevention, and treatment of mental illnesses. Although the past few years have witnessed an increase in the use of AI in the medical practice, its role in psychiatry remains a complex and unanswered question. This chapter provides the current state of knowledge of AI’s use in the diagnosis, prediction, and treatment of psychiatric disorders, and examines the challenges and limitations of this approach in the medical practise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kessler RC, Amminger GP, Aguilar-Gaxiola S, Alonso J, Lee S, Ustun TB. Age of onset of mental disorders: a review of recent literature. Curr Opin Psychiatry. 2007;20(4):359–64.
Trautmann S, Rehm J, Wittchen HU. The economic costs of mental disorders: do our societies react appropriately to the burden of mental disorders? EMBO Rep. 2016;17(9):1245–9.
Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol. 2014;43(2):476–93.
Stein DJ, Lund C, Nesse RM. Classification systems in psychiatry: diagnosis and global mental health in the era of DSM-5 and ICD-11. Curr Opin Psychiatry. 2013;26(5):493–7.
Wakefield JC. Diagnostic issues and controversies in DSM-5: return of the false positives problem. Annu Rev Clin Psychol. 2016;12:105–32.
Krystal JH, State MW. Psychiatric disorders: diagnosis to therapy. Cell. 2014;157(1):201–14.
D’Alfonso S, Santesteban-Echarri O, Rice S, Wadley G, Lederman R, Miles C, et al. Artificial intelligence-assisted online social therapy for youth mental health. Front Psychol. 2017;8:796.
Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–30.
Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB, et al. Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2015;1:15030.
Poulin C, Shiner B, Thompson P, Vepstas L, Young-Xu Y, Goertzel B, et al. Predicting the risk of suicide by analyzing the text of clinical notes. PLoS ONE. 2014;9(1):e85733.
Elvevag B, Foltz PW, Weinberger DR, Goldberg TE. Quantifying incoherence in speech: an automated methodology and novel application to schizophrenia. Schizophr Res. 2007;93(1–3):304–16.
Elvevag B, Foltz PW, Rosenstein M, Lynn ED. An automated method to analyze language use in patients with schizophrenia and their first-degree relatives. J Neurolinguistics. 2010;23(3):270–84.
Landauer T, Dumais S. A solution to Plato’s problem: the latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol Rev. 1997;104:211–40.
Landauer TK, Foltz PW, Laham D. An introduction to latent semantic analysis. Discourse Process. 1998;25(2–3):259–84.
Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Muller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol. 2014;93(1):162–6.
Stevens JR, Prince JB, Prager LM, Stern TA. Psychotic disorders in children and adolescents: a primer on contemporary evaluation and management. Prim Care Companion CNS Disord. 2014;16(2).
Varnik P. Suicide in the world. Int J Environ Res Public Health. 2012;9(3):760–71.
Pestian JP, Sorter M, Connolly B, Bretonnel Cohen K, McCullumsmith C, Gee JT, et al. A machine Learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter Trial. Suicide Life Threat Behav. 2017;47(1):112–21.
Walsh CG, Ribeiro JD, Franklin JC. Predicting risk of suicide attempts over time through machine learning. Clin Psychol Sci. 2017;5(3):457–69.
Carroll KM, Rounsaville BJ. Computer-assisted therapy in psychiatry: be brave-it’s a new world. Curr Psychiatry Rep. 2010;12(5):426–32.
Szanton SL, Thorpe RJ Jr, Gitlin LN. Beat the blues decreases depression in financially strained older African-American adults. Am J Geriatr Psychiatry. 2014;22(7):692–7.
Proudfoot J, Goldberg D, Mann A, Everitt B, Marks I, Gray JA. Computerized, interactive, multimedia cognitive-behavioural program for anxiety and depression in general practice. Psychol Med. 2003;33(2):217–27.
Proudfoot J, Ryden C, Everitt B, Shapiro DA, Goldberg D, Mann A, et al. Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression in primary care: randomised controlled trial. Br J Psychiatry. 2004;185:46–54.
Postel MG, de Haan HA, De Jong CA. E-therapy for mental health problems: a systematic review. Telemed J E Health. 2008;14(7):707–14.
Kumar V, Sattar Y, Bseiso A, Khan S, Rutkofsky IH. The effectiveness of internet-based cognitive behavioral therapy in treatment of psychiatric disorders. Cureus. 2017;9(8):e1626.
Gleeson JF, Alvarez-Jimenez M, Lederman R. Moderated online social therapy for recovery from early psychosis. Psychiatr Serv. 2012;63(7):719.
Gleeson J, Lederman R, Koval P, Wadley G, Bendall S, Cotton S, et al. Moderated online social therapy: a model for reducing stress in carers of young people diagnosed with mental health disorders. Front Psychol. 2017;8:485.
Lederman R, Wadley G, Gleeson JF, Bendall S, Alvarez-Jimenez M. Moderated online social therapy: designing and evaluating. ACM Trans Comput Interact. 2014;2:1–27.
Alvarez-Jimenez M, Bendall S, Lederman R, Wadley G, Chinnery G, Vargas S, et al. On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis. Schizophr Res. 2013;143(1):143–9.
Rice S, Gleeson J, Davey C, Hetrick S, Parker A, Lederman R, et al. Moderated online social therapy for depression relapse prevention in young people: pilot study of a ‘next generation’ online intervention. Early Interv Psychiatry. 2018;12(4):613–25.
Houston TK, Cooper LA, Ford DE. Internet support groups for depression: a 1-year prospective cohort study. Am J Psychiatry. 2002;159(12):2062–8.
McCrone P, Knapp M, Proudfoot J, Ryden C, Cavanagh K, Shapiro DA, et al. Cost-effectiveness of computerised cognitive-behavioural therapy for anxiety and depression in primary care: randomised controlled trial. Br J Psychiatry. 2004;185:55–62.
O’Keeffe GS, Clarke-Pearson K, Council on C, Media. The impact of social media on children, adolescents, and families. Pediatrics. 2011;127(4):800–4.
Dennis CL. Peer support within a health care context: a concept analysis. Int J Nurs Stud. 2003;40(3):321–32.
Weisband S, Kiesler S. Self-disclosure on computer forms: meta-analysis and implications. In: CHI 96 electronic proceedings. https://dl.acm.org/citation.cfm?id=238387.
Conflict of Interest
The author declares that he has no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Fakhoury, M. (2019). Artificial Intelligence in Psychiatry. In: Kim, YK. (eds) Frontiers in Psychiatry. Advances in Experimental Medicine and Biology, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-32-9721-0_6
Download citation
DOI: https://doi.org/10.1007/978-981-32-9721-0_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9720-3
Online ISBN: 978-981-32-9721-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)