Kaygı Bozukluklarının Tanısal Amaçlı Klinik Değerlendirmesi ve Yapay Zekâ

Öz

Bu çalışma kaygı bozukluklarının klinik değerlendirilmesinde yapay zekâ uygulamalarının rolü üzerine güncel bir alanyazın incelemesidir. Kaygı bozuklukları, aşırı korku ve endişe ile karakterize olup kişinin işlevselliğini olumsuz yönde etkileyen psikolojik rahatsızlıklardır. Son yıllarda, yapay zekâ teknolojilerindeki gelişmeler ruh sağlık alanında yeni değerlendirme yaklaşımlarının ortaya çıkmasında etkili olmaktadır. Bu kapsamda, kaygı bozukluklarının tanılanması ve izlenmesinde doğal dil işleme, makine öğrenmesi ve derin öğrenme ile birlikte giyilebilir teknolojiler, sohbet botları, nörogörüntüleme analizleri ve sanal gerçeklik uygulamalarının tanı ve tedavi süreçlerindeki kullanımı incelenmiştir. İncelenen çalışmalar yapay zekâ temelli teknolojilerin fizyolojik belirtilerin anlık takibi, verileri bütüncül olarak işleyebilme, erişilebilirlik ve kişiye özel değerlendirme yapabilme gibi avantajlarının olduğunu göstermektedir. Bununla birlikte veri güvenliği, etik standartlar ve terapötik ilişkideki zorluklar gibi konular bu teknolojilerin temel dezavantajları olarak öne çıkmaktadır. Sonuç olarak, yapay zekâ temelli teknolojiler kaygı bozukluklarının klinik değerlendirilmesinde yüksek bir potansiyel sergilese de bu potansiyelin klinik uygulamalarda güvenli ve etik bir zeminde standardize edilebilmesi için daha kapsamlı çalışmalara ihtiyaç duyulmaktadır.

Anahtar Kelimeler

• kaygı bozuklukları
• yapay zekâ
• klinik değerlendirme
• tanı.

Clinical Assessment of Anxiety Disorders for Diagnostic Purposes and Artificial Intelligence

Öz

This study is a current literature review examining the role of artificial intelligence applications in the clinical assessment of anxiety disorders. Anxiety disorders are psychological conditions characterized by excessive fear and anxiety that negatively affect an individual’s level of functioning. In recent years, advancements in artificial intelligence technologies have contributed to the emergence of new assessment approaches in the field of mental health. In this context, the use of natural language processing, machine learning, and deep learning methods, along with wearable technologies, chatbots, neuroimaging analyses, and virtual reality applications, in the diagnosis and monitoring processes of anxiety disorders has been examined. The reviewed studies indicate that artificial intelligence–based technologies offer advantages such as real-time monitoring of physiological symptoms, the ability to process data in an integrated manner, accessibility, and the capacity to provide personalized assessments. However, issues related to data security, ethical standards, and therapeutic relationship emerge as major limitations of these technologies. In conclusion, although artificial intelligence–based technologies demonstrate high potential in the clinical assessment of anxiety disorders, more comprehensive studies are needed to ensure that this potential can be standardized within clinical practice on a safe and ethical basis.

Anahtar Kelimeler

• anxiety disorders
• artificial intelligence
• clinical assessment
• diagnosis

Kaynakça

1. 1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5. Baskı, metin gözden geçirilmiş Arlington, VA: American Psychiatric Publishing; 2022. https://doi.org/10.1176/appi.books.9780890425787
2. 2. World Health Organization. World mental health report: transforming mental health for all. Geneva: World Health Organization; 2022. https://www.who.int/publications/i/item/9789240049338
3. 3. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988 Aral;56(6):893 897. https://doi.org/10.1037/0022-006X.56.6.893
4. 4. Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol. 1959;32(1):50 55 https://doi.org/10.1111/j.2044-8341.1959.tb00467.x
5. 5. Derogatis LR. SCL 90: Administration, scoring, and procedures manual. 1975.
6. 6. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD Arch Intern Med. 2006 May 22;166(10):1092 1097. doi:10.1001/archinte.166.10.1092
7. 7. Ediboğlu GO. Yapay zekanın insan zekasına psikoterapötik yaklaşımı. Çukurova Tıp Öğrenci Dergisi. 2023;3(1):12 18. https://izlik.org/JA29ZC55XU
8. 8. Turing AM. Computing machinery and intelligence. Mind. 1950;59(236):433 460. https://doi.org/10.1093/mind/LIX.236.433

9. 9. Weizenbaum J. Computer power and human reason: From judgment to calculation. San Francisco, CA: W. H. Freeman; 1976.
10. 10. D’Alfonso S. AI in mental health. Curr Opin Psychol. 2020;36:112 117. https://doi.org/10.1016/j.copsyc.2020.04.005
11. 11. Holohan M, Fiske A. “Like I’m talking to a real person”: exploring the meaning of transference for the use and design of AI based applications in psychotherapy. Front Psychol. 2021;12:720476. https://doi.org/10.3389/fpsyg.2021.720476
12. 12. Bal F. Artificial Intelligence and Psychotherapy. Psikiyatride Güncel Yaklaşımlar. 2025;17(4):643 660. doi: 10.18863/pgy.1538432
13. 13. Linardon J. Navigating the future of psychiatry: A review of research on opportunities, applications, and challenges of artificial intelligence. Curr Treat Options Psychiatry. 2025;12(8):1 7. https://doi.org/10.1007/s40501-025-00344-1
14. 14. 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.
15. 15. Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: Digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017;7(3):e1053. doi:10.1038/tp.2017.25.
16. 16. Taskynbayeva M, Gutoreva A. Machine learning approaches to anxiety detection: trends, model evaluation, and future directions. Front Artif Intell. 2025;8:1630047. doi:10.3389/frai.2025.1630047.
17. 17. Pavlopoulos A, Rachiotis T, Maglogiannis I. An overview of tools and technologies for anxiety and depression management using AI. Appl Sci. 2024;14(19):1 34. https://doi.org/10.3390/app14199068
18. 18. Çömlekci MN. Yılmaz, T. Artificial Intelligence–Based Clinical Assessment in Mood Disorders: A Narrative Review. Current Approaches in Psychiatry, (Advanced Online Publication), 1063-1075. https://doi: 10.18863/pgy.1749490
19. 19. Sadeh Sharvit S, ve ark. Effects of an artificial intelligence platform for behavioral interventions on depression and anxiety symptoms: randomized clinical trial. J Med Internet Res. 2023;25:e46781. https://doi.org/10.2196/46781
20. 20. Tausczik YR, Pennebaker JW. The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol. 2010;29(1):24 54. doi: 10.1177/0261927X09351676
21. 21. Liu Z, ve ark. Listening to mental health crisis needs at scale: using natural language processing to understand and evaluate a mental health crisis text messaging service. Front Digit Health. 2021;3:779091. https://doi.org/10.3389/fdgth.2021.779091
22. 22. Li H, ve ark. Systematic review and meta analysis of AI based conversational agents for promoting mental health and well being. NPJ Digit Med. 2023;6(1):236. https://doi.org/10.1038/s41746-023-00979-5
23. 23. Chan FH, Barry TJ, Chan AB, Hsiao JH. Understanding visual attention to face emotions in social anxiety using hidden Markov models. Cogn Emot. 2020;34(8):1704 1710. https://doi.org/10.1080/02699931.2020.1781599
24. 24. Rook L, Mazza MC, Lefter I, Brazier F. Toward linguistic recognition of generalized anxiety disorder. Front Digit Health. 2022;4:779039. doi:10.3389/fdgth.2022.779039.
25. 25. Zafar F, ve ark. The role of artificial intelligence in identifying depression and anxiety: a comprehensive literature review. J Health Technol. 2024;15(2):123 145. doi: 10.7759/cureus.56472
26. 26. Cheng Y, ve ark. Recent developments in sensors for wearable device applications. Anal Bioanal Chem. 2021;413(24):6037 6057. https://doi.org/10.1007/s00216-021-03602-2
27. 27. Abd alrazaq A, ve ark. Wearable artificial intelligence for detecting anxiety: systematic review and meta analysis. J Med Internet Res. 2023;25:e48754. doi: 10.2196/487542
28. 28. Cruz L, ve ark. A wearable and mobile intervention delivery system for individuals with panic disorder. In: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM ’15). New York, NY: Association for Computing Machinery; 2015:175 182. https://doi.org/10.1145/2836041.2836058
29. 29. Ponzo S, ve ark. Efficacy of the digital therapeutic mobile app biobase to reduce stress and improve mental well being among university students: randomized controlled trial. JMIR MHealth UHealth. 2020 May;8(5):e17767. https://doi.org/10.2196/17767
30. 30. Jacobson NC, Lekkas D, Huang R, Thomas N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J Affect Disord. 2021;282:104 111. https://doi.org/10.1016/j.jad.2020.12.086
31. 31. 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(2):e19. https://doi.org/10.2196/mental.7785
32. 32. Inkster B, Sarda S, Subramanian V. An empathy driven, conversational artificial intelligence agent (Wysa) for digital mental well being: real world data evaluation mixed methods study. JMIR MHealth UHealth. 2018;6(7):e12106. https://doi.org/10.2196/12106
33. 33. Fulmer R, ve ark. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health. 2018;5(2):e64. https://doi.org/10.2196/mental.9782
34. 34. Zhou S, Zhao J, Zhang L. Application of artificial intelligence on psychological interventions and diagnosis: an overview. Front Psychiatry. 2022;13:811665. https://doi.org/10.3389/fpsyt.2022.811665
35. 35.Rootd. Panic Attack and Anxiety Relief [Internet]. 2025 [erişim tarihi 2025 May 22]. Erişim adresi: https://www.rootd.io/
36. 36. Thakkar A, Gupta A, De Sousa A. Artificial intelligence in positive mental health: a narrative review. Front Digit Health. 2024;6:1280235. doi: 10.3389/ fdgth.2024.1280235
37. 37. Rezaei S, ve ark. Machine learning applied to functional magnetic resonance imaging in anxiety disorders. J Affect Disord. 2023;342:54 62. https://doi.org/10.1016/j.jad.2023.09.006
38. 38. Bruin WB, ve ark. Brain based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA Anxiety database using machine learning. Nat Ment Health. 2024;2(1):104 118. https://doi.org/10.1038/s44220-023-00173-2
39. 39. Oing T, Prescott J. Implementations of virtual reality for anxiety related disorders: systematic review. JMIR Serious Games. 2018;6(4):e10965. https://doi.org/10.2196/10965
40. 40. Botella C, ve ark. Recent progress in virtual reality exposure therapy for phobias: a systematic review. Curr Psychiatry Rep. 2017;19:78. doi: 10.1007/s11920-017-0788-4
41. 41. Carl E, ve ark. Virtual reality exposure therapy for anxiety and related disorders: a meta analysis of randomized controlled trials. J Anxiety Disord. 2019;61:27 36. https://doi.org/10.1016/j.janxdis.2018.08.003
42. 42. Freeman D, ve ark. Automated virtual reality therapy to treat agoraphobic avoidance and distress in patients with psychosis (gameChange): a multicentre, parallel group, single blind, randomised, controlled trial in England with mediation and moderation analyses. Lancet Psychiatry. 2022;9(4):293 303. https://doi.org/10.1016/S2215-0366(22)00030-9
43. 43. Navarro Haro MV, ve ark. Meditation experts try virtual reality mindfulness: a pilot study evaluation of the feasibility and acceptability of virtual reality to facilitate mindfulness practice in people attending a mindfulness conference. Front Psychol. 2017;11:577017. https://doi.org/10.1371/journal.pone.0187777
44. 44. Terra M, Baklola M, Ali S, El Bastawisy K. Opportunities, applications, challenges and ethical implications of artificial intelligence in psychiatry: a narrative review. Egypt J Neurol Psychiatry Neurosurg. 2023;59(1):80. https://doi.org/10.1186/s41983-023-00681-z
45. 45. van der Schyff EL, ve ark. Providing self led mental health support through an artificial intelligence–powered chat bot (Leora) to meet the demand of mental health care. J Med Internet Res. 2023;25:e46448. https://doi.org/10.2196/46448
46. 46. Minerva F, Giubilini A. Is AI the future of mental healthcare? Topoi. 2023;42(3):809 817. https://doi.org/10.1007/s11245-023-09932-3
47. 47. Char DS, Abràmoff MD, Feudtner C. Identifying ethical considerations for machine learning healthcare applications. Am J Bioeth. 2020;20(11):7 17. https://doi:10.1080/15265161.2020.1819469.
48. 48. Das KP, Gavade P. A review on the efficacy of artificial intelligence for managing anxiety disorders. Front Artif Intell. 2024;7:1435895. https://doi.org/10.3389/frai.2024.1435895
49. 49. Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V et al. (2018) AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds Mach, 28:689–707. https://doi.org/10.1007/s11023-018-9482-5