Applicability of artificial intelligence in psychiatry: a review of clinical trials
DOI:
https://doi.org/10.25118/2236-918X-10-1-2Keywords:
Artificial intelligence, psychiatry, clinical trialsAbstract
Artificial intelligence (AI), enabled by technological advancement and improvement of computerized systems, aims to allow machines to mimic human capacities at increased speed and accuracy. AI has been shown to be as a useful and efficient tool within the health care scenario,with potentially significant applicability in psychiatry. The objective of this manuscript was to present and discuss the applicability of AI specifically in psychiatry, to help clarify its relevance in the medical practice. In order to do that, a data survey of clinical trials describing the use of AI in psychiatry was conducted, focusing on studies published in the 21st century on the following virtual databases: PubMed, Biblioteca Virtual de Saúde, and MEDLINE. We observed that most of the clinical trials employing AI in psychiatry were conducted to predict pharmacological treatment, followed by other areas such as development of social skills and analysis of structural changes of the central nervous system. The application of AI to medicine represents significant innovations and advances in the field of psychiatry; however, so far, it is not yet a substitute for clinical evaluation.
Downloads
Metrics
References
Brasil, Ministério da Saúde. Saúde mental: o que é, doenças, tratamentos e direitos [Internet]. [cited 2019 Apr 24]. https://saude.gov.br/saude-de-a-z/saude-mental
Falceto OG, Busnello ED, Bozzetti MC. Validação de escalas diagnósti cas do funcionamento familiar para utilização em serviços de atenção primária à saúde. Rev Panam Salud Publica. 2000;7:255-63.3. Del-Ben CM, Rufino AC, Azevedo-Marques JM, Menezes, PR.[Differential diagnosis of first episode psychosis: importance of optimal approach in psychiatric emergencies]. Braz J Psychiatry. 2010;32 Suppl 2:S78-86.
Araújo AC, Lotufo Neto F. A nova classificação americana para os transtornos mentais – o DSM-5. Rev Bras Ter Comp Cogn. 2014;16:67-82.
Insel T. Thomas Mental Health Information: transforming diagnosis [Internet]. 2013 Apr 29 [cited 2019 Apr 25]. www.nimh.nih.gov/about/directors/thomas-insel/blog/2013/transformingdiagnosis.shtml
Lobo Luiz Carlos Inteligência artificial, o Futuro da Medicina e a Educação Médica. Rev Bras Educ Med. 2018; 42: 44-46.
Scott IA. Machine learning and evidence-based medicine. Ann Intern Med. 2018;169:44-6.
Albu A, Stanciu L. Benefits of using artificial intelligence in medical predictions [Internet]. 2015 [cited 2019 Apr 27]. ieeexplore.ieee.org/document/7391610
Dawes TJ, de Marvao A, Shi W, Fletcher T, Watson GM, Wharton J, et al. Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology. 2017;283:381-90.
Cheung CY, Tang F, Ting DS, Tan GS, Wong TY. Artificial intelligence in diabetic eye disease screening. Asia Pac J Ophthalmol (Phila). 2019;8:158-64.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115-8.
Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, et al. The present and future of deep learning in radiology. Eur J Radiol. 2019;114:14-24.
Kim JW, Sharma V, Ryan ND. Predicting methylphenidate response in ADHD using machine learning approaches. Int J Neuropsychopharmacol. 2015 May 10;18(11):pyv052. doi: 10.1093/ijnp/pyv052.
Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P, Wobrock T, et al. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry. 2016;3:935-46.
Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3:243-50.
Bak N, Ebdrup BH, Oranje B, Fagerlund B, Jensen MH, Düring SW, et al. Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology. Transl Psychiatry. 2017;7:e1087.
Lenhard F, Sauer S, Andersson E, Månsson KN, Mataix-Cols D, Rück C, et al. Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: a machine learning approach. Int J Methods Psychiatr Res. 2018 Mar;27(1). doi: 10.1002/mpr.1576. Epub 2017 Jul 28.
Carrillo F, Sigman M, Fernández Slezak D, Ashton P, Fitzgerald L, Stroud J, et al. Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. J Affect Disord. 2018;230:84-6.
Pestian JP, Grupp-Phelan J, Bretonnel Cohen K, Meyers G, Richey LA, Matykiewicz P, et al. A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department. Suicide Life Threat Behav. 2016;46:154-9.
Dazzan P. Neuroimaging biomarkers to predict treatment response in schizophrenia: the end of 30 years of solitude? Dialogues Clin Neurosci. 2014;16:491-503.
Zhang W, Yang X, Lui S, Meng Y, Yao L, Xiao Y, et al. Diagnostic prediction for social anxiety disorder via multivariate pattern analysis of the regional homogeneity. Biomed Res Int. 2015;2015:763965.
Schnack HG, Nieuwenhuis M, van Haren NE, Abramovic L, Scheewe TW, Brouwer RM, et al. Can structural MRI aid in clinical classifi cati on? A machine learning study in two independent samples of pati ents with schizophrenia, bipolar disorder and healthy subjects. Neuroimage. 2014; 84:299-306.
Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Bürger C, et al. Predicti on of individual response to electroconvulsive therapy via machine learning on structural magneti c resonance imaging data. JAMA Psychiatry. 2016;73:557-64.
Yun SS, Choi J, Park SK, Bong GY, Yoo H. Social skills training for children with auti sm spectrum disorder using a roboti c behavioral interventi on system. Auti sm Res. 2017;10:1306-23.
Voss C, Schwartz J, Daniels J, Kline A, Haber N, Washington P, et al. Eff ect of wearable digital interventi on for improving socializati on in children with auti sm spectrum disorder: a randomized clinical trial. JAMA Pediatr. 2019;173:446-54.
Mengoni SE, Irvine K, Thakur D, Barton G, Dautenhahn K, Guldberg K, et al. Feasibility study of a randomized controlled trial to investi gate the eff ecti veness of using a humanoid robot to improve the social skills of children with auti sm spectrum disorder (Kaspar RCT): a study protocol. BMJ Open. 2017;7:e017376.
Wesemann U, Kowalski JT, Jacobsen T, Beudt S, Jacobs H, Fehr J, et al. Evaluati on of a technologybased adapti ve learning and preventi on program for stress response -- a randomized controlled trial. Mil Med. 2016;181:863-71.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Debates em Psiquiatria allows the author (s) to keep their copyrights unrestricted. Allows the author (s) to retain their publication rights without restriction. Authors should ensure that the article is an original work without fabrication, fraud or plagiarism; does not infringe any copyright or right of ownership of any third party. Authors should also ensure that each one complies with the authorship requirements as recommended by the ICMJE and understand that if the article or part of it is flawed or fraudulent, each author shares responsibility.
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) - Debates em Psiquiatria is governed by the licencse CC-By-NC
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.