Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları

Turan, Bahadır; Gülşen, Murat; Yilmaz, Asim Egemen

doi:10.4274/atfm.galenos.2022.36002

Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları

Bahadır TURAN, (Karadeniz Teknik Üniversitesi Tıp Fakültesi, Çocuk ve Ergen Psikiyatri Anabilim Dalı, Trabzon, Türkiye - Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Disiplinler Arası Yapay Zeka Teknolojileri Anabilim Dalı, Ankara, Türkiye)

Murat GÜLŞEN, (Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Disiplinler Arası Yapay Zeka Teknolojileri Anabilim Dalı, Ankara, Türkiye - Sağlık Bakanlığı Sağlık Hizmetleri Genel Müdürlüğü, Otizm, Zihinsel Özel Gereksinimler ve Nadir Hastalıklar Dairesi, Ankara, Türkiye)

Asım Egemen YILMAZ (Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Disiplinler Arası Yapay Zeka Teknolojileri Anabilim Dalı, Ankara, Türkiye)

Ankara Üniversitesi Tıp Fakültesi Mecmuası

35 4

Yıl: 2022 Cilt: 75 Sayı: 1 Sayfa Aralığı: 56 - 62 Metin Dili: Türkçe DOI: 10.4274/atfm.galenos.2022.36002 İndeks Tarihi: 04-05-2023

Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları

Öz:

Ruh sağlığı alanında objektif tanısal değerlendirme ve etkili müdahalelerin geliştirilmesine yardımcı olabilecek fenomenleri takip etmek ve bu alana hem fiziksel hem de zihinsel yatırım yapabilen bir anlayışa sahip olmak önem taşımaktadır. Bu bağlamda yapay zekanın psikiyatride kullanımına yönelik araştırmalar son yıllarda dikkat çekmekte ve gittikçe artmaktadır. Bu yazıda çeşitli psikiyatrik bozukluklarda yapay zekanın rolü, ruh sağlığı alanında yapay zekanın mevcut durumu ve uygulamaları, yapay zeka uygulamaları konusundaki fırsatlar ve sınırlılıklar hakkında mevcut literatür ışığında bilgiler gözden geçirilecektir.

Anahtar Kelime:

Artificial Intelligence Applications in Psychiatric Disorders

Öz:

Following the phenomena that can help develop objective diagnostic assessments and effective interventions and thus, having an understanding that can invest both physically and mentally in the field of psychiatry is crucial. In this context, research on the use of artificial intelligence in psychiatry has drawn attention in recent years. In this article, information about the role of artificial intelligence in various psychiatric disorders, the current status and applications of artificial intelligence in the field of mental health, the opportunities and limitations of artificial intelligence applications will be reviewed in the light of the existing literature.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Derleme Erişim Türü: Erişime Açık

1. Pang Z, Yuan H, Zhang Y-T, et al. Guest editorial health engineering driven by the industry 4.0 for aging society. IEEE Journal of Biomedical and Health Informatics. 2018;22:1709-1710.
2. Schwab K. The fourth industrial revolution: Currency; 2017.
3. Simon HA. Artificial intelligence: where has it been, and where is it going? IEEE Transactions on Knowledge and Data Engineering. 1991;3:128-136.
4. Georgian-Smith D, Moore RH, Halpern E, et al. Blinded comparison of computer-aided detection with human second reading in screening mammography. AJR Am J Roentgenol. 2007;189:1135-1141.
5. Robert C. Machine learning, a probabilistic perspective. Taylor & Francis. Chance. 2014;27:62-63.
6. Goodfellow I, Bengio Y, Courville A. Deep learning: MIT press; 2016.
7. UC Berkeley MT. What Is Machine Learning (ML)? 2020 [Available from: https://ischoolonline.berkeley.edu/blog/what-is-machine-learning/.
8. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117.
9. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230-243.
10. Beam AL, Kohane IS. Translating Artificial Intelligence Into Clinical Care. JAMA. 2016;316:2368-2369.
11. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19:1236-1246.
12. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56.
13. Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112:22-28.
14. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40.
15. Intel Corporation. Overcoming Barriers in AI Adoption in Healthcare 2018 [Available from: https://newsroom.intel.com/wp-content/uploads/ sites/11/2018/07/healthcare-iot-infographic.pdf.
16. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500-510.
17. Sengupta PP, Adjeroh DA. Will Artificial Intelligence Replace the Human Echocardiographer? Circulation. 2018;138:1639-1642.
18. Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change. 2018;126:3-13.
19. Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018;131:129-133.
20. Gabbard GO, Crisp-Han H. The Early Career Psychiatrist and the Psychotherapeutic Identity. Acad Psychiatry. 2017;41:30-34.
21. Bzdok D, Meyer-Lindenberg A. Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:223-230.
22. Jeste DV, Glorioso D, Lee EE, et al. Study of Independent Living Residents of a Continuing Care Senior Housing Community: Sociodemographic and Clinical Associations of Cognitive, Physical, and Mental Health. Am J Geriatr Psychiatry. 2019;27:895-907.
23. Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49:1426- 1448.
24. Janssen RJ, Mourão-Miranda J, Schnack HG. Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:798-808.
25. Luxton DD. Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice. 2014;45:332-339.
26. 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.
27. Antonucci LA, Raio A, Pergola G, et al. Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition. BMC Psychol. 2021;9:47.
28. Sadeghi D, Shoeibi A, Ghassemi N, et al. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med. 2022;146:105554.
29. Lai JW, Ang CKE, Acharya UR, et al. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. Int J Environ Res Public Health. 2021;18:6099.
30. Elvevåg B, Foltz PW, Weinberger DR, et al. Quantifying incoherence in speech: an automated methodology and novel application to schizophrenia. Schizophr Res. 2007;93:304-316.
31. Bain EE, Shafner L, Walling DP, et al. Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia. JMIR Mhealth Uhealth. 2017;5:e18.
32. Dellazizzo L, Percie du Sert O, Phraxayavong K, et al. Exploration of the dialogue components in Avatar Therapy for schizophrenia patients with refractory auditory hallucinations: A content analysis. Clin Psychol Psychother. 2018;25:878-885.
33. du Sert OP, Potvin S, Lipp O, et al. Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: A pilot clinical trial. Schizophr Res. 2018;197:176-181.
34. Dikeç G, Oban V, Usta MB. Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related to Schizophrenia. Turkish Journal of Psychiatry. 2021. Online Published.
35. Saeedi A, Saeedi M, Maghsoudi A, et al. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn. 2021;15:239-252.
36. Al Jazaery M, Guo G. Video-based depression level analysis by encoding deep spatiotemporal features. IEEE Transactions on Affective Computing. 2018;12:262-268.
37. Delnevo G, Mancini G, Roccetti M, et al. The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study. Sensors (Basel). 2021;21:2361.
38. Sánchez-Morla EM, Fuentes JL, Miguel-Jiménez JM, et al. Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence. J Pers Med. 2021;11:803.
39. Choi J, Bodenstein DF, Geraci J, et al. Evaluation of postmortem microarray data in bipolar disorder using traditional data comparison and artificial intelligence reveals novel gene targets. J Psychiatr Res. 2021;142:328-336.
40. Sachan D. Self-help robots drive blues away. Lancet Psychiatry. 2018;5:547.
41. Fitzpatrick KK, Darcy A, Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Ment Health. 2017;4:e19.
42. Bone D, Goodwin MS, Black MP, et al. Applying machine learning to facilitate autism diagnostics: pitfalls and promises. J Autism Dev Disord. 2015;45:1121-1136.
43. Coe BP, Girirajan S, Eichler EE. The genetic variability and commonality of neurodevelopmental disease. Am J Med Genet C Semin Med Genet. 2012;160C:118-129.
44. Kosmicki JA, Sochat V, Duda M, et al. Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Transl Psychiatry. 2015;5:e514.
45. Wall DP, Dally R, Luyster R, et al. Use of artificial intelligence to shorten the behavioral diagnosis of autism. PLoS One. 2012;7:e43855.
46. Usta MB, Karabekiroglu K, Sahin B, et al. Use of machine learning methods in prediction of short-term outcome in autism spectrum disorders. Psychiatry and Clinical Psychopharmacology. 2019;29:320-325.
47. Ucuz İ, Uzun Çiçek A. Artificial neural networks based-prediction of autism spectrum disorder. The Journal of Cognitive Systems. 2020;5:78-82.
48. Quinodoz M, Royer-Bertrand B, Cisarova K, et al. DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders. Am J Hum Genet. 2017;101:623-629.
49. Smyser CD, Dosenbach NU, Smyser TA, et al. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage. 2016;136:1-9.
50. Crippa A, Salvatore C, Perego P, et al. Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. J Autism Dev Disord. 2015;45:2146-2156.
51. Duda M, Ma R, Haber N, et al. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry. 2016;6:e732.
52. Amato F, Di Gregorio M, Monaco C, editors. Socially assistive robotics combined with artificial intelligence for ADHD. 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC); 2021: IEEE.
53. Anuradha J, Ramachandran V, Arulalan K, editors. Diagnosis of ADHD using SVM algorithm. Proceedings of the Third Annual ACM Bangalore Conference; 2010.
54. Berrezueta-Guzman J, Robles-Bykbaev V, Pau I, et al. Robotic Technologies in ADHD Care: Literature Review. IEEE Access. 2021.
55. Yoo H. Genetics of Autism Spectrum Disorder: Current Status and Possible Clinical Applications. Exp Neurobiol. 2015;24:257-272.
56. Engchuan W, Dhindsa K, Lionel AC, et al. Performance of case-control rare copy number variation annotation in classification of autism. BMC Med Genomics. 2015;8(Suppl 1):S7.
57. Zhou J, Park CY, Theesfeld CL, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019;51:973-980.
58. Mahajan R, Mostofsky SH. Neuroimaging endophenotypes in autism spectrum disorder. CNS Spectr. 2015;20:412-426.
59. Xiao X, Fang H, Wu J, et al. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res. 2017;10:620-630.
60. Song DY, Kim SY, Bong G, et al. The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review. Soa Chongsonyon Chongsin Uihak. 2019;30:145-152.
61. Loth E, Garrido L, Ahmad J, et al. Facial expression recognition as a candidate marker for autism spectrum disorder: how frequent and severe are deficits? Mol Autism. 2018;9:7.
62. Liu W, Li M, Yi L. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Res. 2016;9:888-898.
63. Mengi M, Malhotra D. Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review. Archives of Computational Methods in Engineering. 2022;29:2811-2855.
64. Graham S, Depp C, Lee EE, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep. 2019;21:116.
65. Kalanderian H, Nasrallah HA. Artificial intelligence in psychiatry. Current Psychiatry. 2019;18:33-38.
66. Kessler RC, Warner CH, Ivany C, et al. Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS). JAMA Psychiatry. 2015;72:49- 57.
67. Pestian JP, Grupp-Phelan J, Bretonnel Cohen K, 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;46:154-159.
68. Roy A, Nikolitch K, McGinn R, et al. A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med. 2020;3:78.
69. Bernert RA, Hilberg AM, Melia R, et al. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. Int J Environ Res Public Health. 2020;17:5929.
70. Houston TK, Cooper LA, Ford DE. Internet support groups for depression: a 1-year prospective cohort study. Am J Psychiatry. 2002;159:2062-2068.
71. Luxton DD. An introduction to artificial intelligence in behavioral and mental health care. Artificial intelligence in behavioral and mental health care: Elsevier; 2016. p. 1-26.
72. Bower P, Gilbody S. Stepped care in psychological therapies: access, effectiveness and efficiency. Narrative literature review. Br J Psychiatry. 2005;186:11-7.
73. Luxton DD. Recommendations for the ethical use and design of artificial intelligent care providers. Artif Intell Med. 2014;62:1-10.
74. Ray A, Bhardwaj A, Malik YK, et al. Artificial intelligence and Psychiatry: An overview. Asian J Psychiatr. 2022;70:103021.
75. Fakhoury M. Artificial Intelligence in Psychiatry. Adv Exp Med Biol. 2019;1192:119-125.
76. Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. J Med Internet Res. 2019;21:e13216.
77. Cresswell K, Cunningham-Burley S, Sheikh A. Health Care Robotics: Qualitative Exploration of Key Challenges and Future Directions. J Med Internet Res. 2018;20:e10410.
78. Özdemir V. Not All Intelligence is Artificial: Data Science, Automation, and AI Meet HI. OMICS. 2019;23:67-69.
79. De Choudhury M, Kiciman E. Integrating artificial and human intelligence in complex, sensitive problem domains: Experiences from mental health. AI Magazine. 2018;39:69-80.

APA	Turan B, Gülşen M, Yilmaz A (2022). Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. , 56 - 62. 10.4274/atfm.galenos.2022.36002
Chicago	Turan Bahadır,Gülşen Murat,Yilmaz Asim Egemen Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. (2022): 56 - 62. 10.4274/atfm.galenos.2022.36002
MLA	Turan Bahadır,Gülşen Murat,Yilmaz Asim Egemen Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. , 2022, ss.56 - 62. 10.4274/atfm.galenos.2022.36002
AMA	Turan B,Gülşen M,Yilmaz A Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. . 2022; 56 - 62. 10.4274/atfm.galenos.2022.36002
Vancouver	Turan B,Gülşen M,Yilmaz A Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. . 2022; 56 - 62. 10.4274/atfm.galenos.2022.36002
IEEE	Turan B,Gülşen M,Yilmaz A "Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları." , ss.56 - 62, 2022. 10.4274/atfm.galenos.2022.36002
ISNAD	Turan, Bahadır vd. "Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları". (2022), 56-62. https://doi.org/10.4274/atfm.galenos.2022.36002

APA	Turan B, Gülşen M, Yilmaz A (2022). Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. Ankara Üniversitesi Tıp Fakültesi Mecmuası, 75(1), 56 - 62. 10.4274/atfm.galenos.2022.36002
Chicago	Turan Bahadır,Gülşen Murat,Yilmaz Asim Egemen Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. Ankara Üniversitesi Tıp Fakültesi Mecmuası 75, no.1 (2022): 56 - 62. 10.4274/atfm.galenos.2022.36002
MLA	Turan Bahadır,Gülşen Murat,Yilmaz Asim Egemen Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. Ankara Üniversitesi Tıp Fakültesi Mecmuası, vol.75, no.1, 2022, ss.56 - 62. 10.4274/atfm.galenos.2022.36002
AMA	Turan B,Gülşen M,Yilmaz A Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2022; 75(1): 56 - 62. 10.4274/atfm.galenos.2022.36002
Vancouver	Turan B,Gülşen M,Yilmaz A Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2022; 75(1): 56 - 62. 10.4274/atfm.galenos.2022.36002
IEEE	Turan B,Gülşen M,Yilmaz A "Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları." Ankara Üniversitesi Tıp Fakültesi Mecmuası, 75, ss.56 - 62, 2022. 10.4274/atfm.galenos.2022.36002
ISNAD	Turan, Bahadır vd. "Psikiyatrik Bozukluklarda Yapay Zeka Uygulamaları". Ankara Üniversitesi Tıp Fakültesi Mecmuası 75/1 (2022), 56-62. https://doi.org/10.4274/atfm.galenos.2022.36002