Derleme
BibTex RIS Kaynak Göster

Artificial Intelligence and Psychotherapy

Yıl 2025, Cilt: 17 Sayı: 4, 643 - 660

Öz

With the development of artificial intelligence technologies, changes have also begun to be seen in psychotherapy. Although artificial intelligence does not currently have a major impact on the therapy field, it raises major questions about the nature of therapy and the value of the relationship future between people and therapists in understanding how artificial intelligence can be included in the therapy process, foreseeing the future, and being proactive are gaining importance. This article will examine current artificial intelligence applications used in psychotherapy fields with a literature review. Artificial intelligence can be used to increase the effectiveness of psychotherapy. However, it should not be forgotten that excessive reliance on artificial intelligence can overshadow the human aspect of psychotherapy and that the human factor is important. Although there are still uncertainties about how the profession will be affected by the use of artificial intelligence in psychotherapy and how it will be incorporated into therapy processes, it is envisaged that artificial intelligence can play a versatile role in psychotherapy.

Kaynakça

  • Abd-Alrazaq AA, Rababeh A, Alajlani M, Bewick BM, Househ M (2020) Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis. J Med Internet Res, 22:e16021.
  • Abd-Alrazaq A, AlSaad R, Harfouche M, Aziz S, Ahmed A, Damseh R. et al. (2023) Wearable artificial intelligence for detecting anxiety: systematic review and meta-analysis. J Med Internet Res, 25:e48754.
  • Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F (2022) Investigating the feasibility of assessing depression severity and valence-arousal with wearable sensors using discrete wavelet transforms and machine learning. Information, 13:406.
  • Andrews G, Issakidis C, Carter G. (2001) Shortfall in mental health service utilisation. Br J Psychiatry, 179:417-425.
  • Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 145:137-165.
  • Ashar YK, Clark J, Gunning FM, Goldin P, Gross JJ, Wager TD (2021) Brain markers predicting response to cognitive‐behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015. Transl Psychiatry, 11:260.
  • Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J et al. (2017) 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, 5:e18.
  • Bălan O, Moise G, Moldoveanu A, Leordeanu M, Moldoveanu F (2020) An investigation of various machine and deep learning techniques applied in automatic fear level detection and acrophobia virtual therapy. Sensors, 20:496.
  • Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela, JP (2018) Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology, 43:1660-1666.
  • Bayramlı I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW et al. (2022) Predictive structured-unstructured interactions in EHR models: A case study of suicide prediction. NPJ Digit Med, 5:15.
  • Bendig E, Erb B, Schulze-Thuesing L, Baumeister H (2022) The next generation: chatbots in clinical psychology and psychotherapy to foster mental health–a scoping review. Verhaltenstherapie, 32:64-76.
  • Bickman L (2020) Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health. Adm Policy Ment Health, 47:795-843.
  • Birk RH, Samuel G (2022) Digital phenotyping for mental health: reviewing the challenges of using data to monitor and predict mental health problems. Curr Psychiatry Rep, 24:523-528.
  • Biswas A, Talukdar W (2024) Intelligent clinical documentation: harnessing generative AI for patientcentric clinical note generation. Int J Innov Sci Res Technol, 9:994-1008.
  • Blader SL, Rothman NB (2014) Paving the road to preferential treatment with good intentions: Empathy, accountability and fairness. J Exp Soc Psychol, 50:65-81.
  • Braga A, Logan RK (2017) The emperor of strong AI has no clothes: limits to artificial intelligence. Information, 8:156.
  • Bzdok D, Meyer-Lindenberg A (2018) Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging, 3:223-230.
  • Calderita LV, Manso LJ, Bustos P, Suárez CM, Fernández F, Bandera A (2014) Therapist: towards an autonomous socially interactive robot for motor and neurorehabilitation therapies for children. JMIR Rehabil Assist Technol, 1:e1.
  • Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A et al. (2024) Illusory generalizability of clinical prediction models. Sci, 383:164-167.
  • Chen J, Li Y, Wu X, Liang Y, Jha S (2020) Robust out-of-distribution detection for neural networks. arXiv:2003.09711v6.
  • Coghlan S, Leins K, Sheldrick S, Cheong M, Gooding P, D'Alfonso S (2023) To chat or bot to chat: ethical issues with using chatbots in mental health. Digit Health, 9:20552076231183542.
  • Coiera E, Liu S (2022) Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med, 3:100860.
  • Craig TK, Rus-Calafell M, Ward T, Leff JP, Huckvale M, Howarth E et al. (2018) AVATAR therapy for auditory verbal hallucinations in people with psychosis: a single-blind, randomised controlled trial. Lancet Psychiatry, 5:31-40.
  • Crawford K (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Connecticut, Yale University Press.
  • Cuff BM, Brown SJ, Taylor L, Howat DJ (2016) Empathy: a review of the concept. Emotion, 8:144-153.
  • Cuthbert BN (2020) The role of RDoC in future classification of mental disorders. Dialogues Clin Neurosci, 22:81-85.
  • D’Alfonso S (2020) AI in mental health. Curr Opin Psychol, 36:112–117.
  • Dellazizzo L, Percie du Sert O, Phraxayavong K, Potvin S, O'Connor K, Dumais A (2018) Exploration of the dialogue components in Avatar Therapy for schizophrenia patients with refractory auditory hallucinations: a content analysis. Clin Psychol Psychother, 25:878-885.
  • Dogrucu A, Perucic A, Isaro A, Ball D, Toto E, Rundensteiner EA et al. (2020) Moodable: on feasibility of instantaneous depression assessment using machine learning on voice samples artificial intelligence, machine learning and mental healthcare 29 with retrospectively harvested smartphone and social media data. Smart Health, 17:100118.
  • Dong Y, Hou J, Zhang N, Zhang M (2020) Research on how human intelligence, consciousness, and cognitive computing affect the development of Artificial Intelligence. Complexity, 2020:1680845.
  • du Sert, OP, Potvin S, Lipp O, Dellazizzo L, Laurelli M, Breton R et al. (2018) Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: a pilot clinical trial. Schizophr Res, 97:176-181.
  • Dwyer DB, Falkai P, Koutsouleris N (2018) Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol, 14:91-118.
  • Eisenstadt M, Liverpool S, Infanti E, Ciuvat RM, Carlsson C (2021) Mobile apps that promote emotion regulation, positive mental health, and well-being in the general population: systematic review and meta-analysis. JMIR Ment Health, 8:e31170.
  • Farahany NA (2023) The Battle for Your Brain. New York, St. Martin’s Press.
  • Fiske A, Henningsen P, Buyx A (2019) Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J Med Internet Res, 21:e13216.
  • Fitzpatrick KK, Darcy A, Vierhile M (2017) 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, 4:e19.
  • Freeman D, Haselton P, Freeman J, Spanlang B, Kishore S, Albery E et al. (2018) Automated psychological therapy using immersive virtual reality for treatment of fear of heights: a single-blind, parallel-group, randomised controlled trial. Lancet Psychiatry, 5:625-632.
  • Giguère S, Potvin S, Beaudoin M, Dellazizzo L, Giguère CÉ, Furtos A et al. (2023) Avatar intervention for cannabis use disorder in ındividuals with severe mental disorders: A pilot study. J Pers Med, 13:766.
  • Góngora SA, Hamrioui S, de la Torre Díez, I., Motta Cruz E, López MC, Franco M (2019) Social robots for people with aging and dementia: A systematic review of literature. Telemed J E Health, 25:533-540.
  • Gual-Montolio P, Jaén I, Martínez-Borba V, Castilla D, Suso-Ribera C (2022) Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real or close to real-time: A systematic review. Int J Environ Res Public Health, 19:7737.
  • He Y, Yang L, Qian C, Li T, Su Z, Zhang Q et al. (2023) Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res, 25:e43862.
  • Holohan M, Fiske A (2021) 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, 12:720476.
  • 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, 6:e12106.
  • Islam MR, Sakib MKH, Ulhaq A, Akter S, Zhou J, Asirvathamt D (2023) Sidvis: designing visual interactive system for analyzing suicide ideation detection. 2023 27th International Conference Information Visualisation (IV), Tampere, Finland, 2023, 384-389.
  • Kessler RC, Aguilar-Gaxiola S, Alonso J, Chatterji S, Lee S, Ormel J et al. (2009) The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys. Epidemiol Psychiatr Sci, 18:23-33.
  • Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R et al. (2021) Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry, 78:195-209.
  • Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. (2022) From promise to practice: Towards the realisation of AI-informed mental health care. Lancet, 4:e829-e840.
  • Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann, S, Rosen M, Ruef A, Dwyer DB et al. (2018) Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry, 75:1156-1172
  • Lambert MJ (2004) Bergin and Garfield’s Handbook of Psychotherapy and Behavior Change. New York, Wiley.
  • Lamichhane B, Moukaddam N, Sabharwal A. (2024) Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Sci Rep, 14:18808.
  • Li H, Zhang R, Lee YC, Kraut RE, Mohr DC (2023) Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and wellbeing. NPJ Digit Med, 6:236-236.
  • Liu YS, Chokka S, Cao B, Chokka PR (2021a) Screening for bipolar disorder in a tertiary mental health centre using EarlyDetect: a machine learning-based pilot study. J Affect Disord Rep, 6:100215.
  • Liu Y, Hankey J, Cao B, Chokka P (2021b) Screening for major depressive disorder in a tertiary mental health centre using EarlyDetect: a machine learning-based pilot study. J Affect Disord Rep, 3:100062.
  • Lucas GM, Rizzo A, Gratch J, Scherer S, Stratou G, Boberg J et al. (2017) Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Front Robot AI, 4:51.
  • Luxton DD (2014) Artificial intelligence in psychological practice: current and future applications an implications. Prof Psychol Res Pr, 45:332-339.
  • Martin NM, Kreitmair K (2018) Ethical issues for direct-to-consumer digital psychotherapy apps: addressing accountability, data protection, and consent. JMIR Ment Health, 5:e32.
  • Martinez-Martin N, Kreitmair K (2018) Ethical issues for direct-to-consumer digital psychotherapy apps: addressing accountability, data protection, and consent. JMIR Ment Health, 5:e9423.
  • Mehrotra S, Kumar S, Sudhir P, Rao GN, Thirthalli J, Gandotra A (2017) Unguided mental health self-help apps: reflections on challenges through a clinician's lens. Indian J Psychol Med, 39:707-711.
  • Meyerhoff J, Liu T, Kording KP, Ungar LH, Kaiser SM, Karr CJ et al. (2021) Evaluation of changes in depression, anxiety, and social anxiety using smartphone sensor features: longitudinal cohort study. J Med Internet Res, 23:e22844.
  • Minerva F, Giubilini A (2023) Is AI the future of mental healthcare? Topoi, 42:809-817.
  • Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F (2023) Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: a systematic literature review. J Biomed Inform, 138:104278.
  • Nathan PE, Gorman JM (2015) A Guide to Treatments That Work. Oxford, Oxford University Press.
  • Nemesure MD, Heinz MV, Huang R, Jacobson NC (2021) Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep, 11:1980.
  • Olawade DB, Wada OZ, Odetayo A, David-Olawade AC, Asaolu F, Eberhardt J (2024) Enhancing mental health with artificial intelligence: current trends and future prospects. J Med Surg Public Health, 3:100099.
  • Oyebode O, Alqahtani F, Orji R (2020) Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access, 8:111141-111158.
  • Raamkumar AS, Yang Y (2022) Empathetic conversational systems: a review of current advances, gaps, and opportunities. IEEE Trans Affect Comput, 14:2722-2739.
  • Reece AG, Danforth CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Sci, 6:15.
  • Rein BA, McNeil DW, Hayes AR, Hawkins TA, Ng H. Mei, Yura CA (2018) Evaluation of an avatar-based training program to promote suicide prevention awareness in a college setting. J Am Coll Health, 66:401-411.
  • Richards D (2024) Artificial intelligence and psychotherapy: a counterpoint. Couns Psychother Res, https://doi.org/10.1002/capr.12758
  • Richter T, Fishbain B, Fruchter E, Richter-Levin G, Okon-Singer H (2021) Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. J Psychiatr Res, 141:199-205.
  • Roa TM, Biller-Andorno N, Trachsel M (2021) The ethics of artificial intelligence in psychotherapy. In The Oxford Handbook of Psychotherapy Ethics. (Eds M Trachsel, J Gaab, N Biller-Andorno, Ş Tekin, JZ Sadler):744-758. Oxford, Oxford University Press.
  • Roberts LW, Chan S, Torous J (2018) New tests, new tools: mobile and connected technologies in advancing psychiatric diagnosis. NPJ Digit Med, 1:20176.
  • Rosellini AJ, Liu S, Anderson GN, Sbi S, Tung ES, Knyazhanskaya E (2020) Developing algorithms to predict adult onset internalizing disorders: an ensemble learning approach. J Psychiatr Res, 121:189-196.
  • Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA (2020) A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med, 3:1-12.
  • Rozek DC, Andres WC, Smith NB, Leifker FR, Arne K, Jennings G et al. (2020) Using machine learning to predict suicide attempts in military personnel. Psychiatry Res, 294:113515.
  • Sachan D (2018) Self-help robots drive blues away. Lancet Psychiatry, 5:547.
  • Sajjadian M, Lam RW, Milev R, Rotzinger S, Frey BN, Soares CN et al. (2021) Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis. Psychol Med, 51:2742-2751.
  • Schwartz B, Cohen, ZD, Rubel JA, Zimmermann D, Wittmann WW, Lutz W (2021) Personalized treatment selection in routine care: integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychother Res, 31:33-51.
  • Shah J, DePietro B, D'Adamo L, Firebaugh ML, Laing O, Fowler LA et al. (2022) Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening. Int J Eat Disord, 55:1229-1244.
  • Shatte AB, Hutchinson D, Teague SJ. (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med, 49:1426-1448.
  • Torjesen I. (2017) Sixty seconds on . . . sex with robots. Br Med J, 358:j3353.
  • Sönmez D, Hocaoğlu C. (2024) Metaverse and psychiatry: a review. Psikiyatride Güncel Yaklaşımlar, 16:225-238.
  • Srinivasan R, González BSM (2022) The role of empathy for artificial intelligence accountability. J Responsib Technol, 9:100021.
  • Stade EC, Stirman, SW, Ungar LH, Boland CL, Schwartz HA, Yaden DB et al. (2024) Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. Npj Ment Health Res, 3:12.
  • Roa TM, Biller-Andorno N, Trachsel M (2021) The ethics of artificial intelligence in psychotherapy. In The Oxford Handbook of Psychotherapy Ethics. (Eds M Trachsel, J Gaab, N Biller-Andorno, Ş Tekin, JZ Sadler):744-758. Oxford, Oxford University Press.
  • Stark L, Hoey J. (2021) The ethics of emotion in artificial intelligence systems. In FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency: 782-793. New York, Association for Computing Machinery. New York, United States.
  • Su C, Aseltine R, Doshi R, Chen K, Rogers SC, Wang F. (2020) Machine learning for suicide risk prediction in children and adolescents with electronic health records. Transl Psychiatry, 10:413.
  • Swaminathan A, López I, Mar RAG, Heist T, McClintock T, Caoili K et al. (2023) Natural language processing system for rapid detection and intervention of mental health crisis chat messages. NPJ Med, 6:213.
  • Taliaz D, Spinrad A, Barzilay R, Barnett-Itzhaki Z, Averbuch D, Teltsh O et al. (2021) Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry, 11:381.
  • Teding van Berkhout E, Malouff JM (2016) The efficacy of empathy training: a meta-analysis of randomized controlled trials. J Couns Psychol, 63:32-41.
  • Thakkar A, Gupta A, De Sousa A. (2024) Artificial intelligence in positive mental health: a narrative review. Front Digit Health, 6:1280235.
  • Turing AM (1950) Computing machinery and intelligence. Mind, 59:433-460.
  • Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I et al. (2023) An AI-based decision support system for predicting mental health disorders. Inf Syst Front, 25:1261-1276.
  • van den Bosch K, Bronkhorst K (2019) Six challenges for human-AI co-learning. Adaptive Instructional Systems, 11597:572–589.
  • Wada K, Shibata T (2007) Living with seal robots—its sociopsychological and physiological influences on the elderly at a care house. IEEE Trans Robot, 23:972-980.
  • Wang L, Fagan C, Yu CI (2020) Popular mental health apps (MH apps) as a complement to telepsychotherapy: guidelines for consideration. J Psychother Integr, 30:265-273.
  • Ward T, Rus-Calafell M, Ramadhan Z, Soumelidou O, Fornells-Ambrojo M, Garety P et al. (2020) AVATAR therapy for distressing voices: a comprehensive account of therapeutic targets. Schizophr Bull, 46:1038-1044.
  • Weizenbaum J. (1976) Computer Power and Human Reason: From Judgment to Calculation. London, Freeman.
  • Wenzel J, Haas SS, Dwyer DB, Ruef A, Oeztuerk OF, Antonucci LA. et al. (2021) Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints? Neuropsychopharmacology, 46:1475-1483.
  • Wiebe A, Aslan B, Brockmann C, Lepartz A, Dudek D, Kannen K et al. (2023) Multimodal assessment of adult attention‐deficit hyperactivity disorder: a controlled virtual seminar room study. Clin Psychol Psychother, 30:1111-1129.
  • Wong WH (2023) We, The Data: Human Rights in The Digital Age. Massachusetts, MIT Press.
  • Yu R, Hui E, Lee J, Poon D, Ng A, Sit K et al. (2015) Use of a therapeutic, socially assistive pet robot (PARO) in improving mood and stimulating social interaction and communication for people with dementia: study protocol for a randomized controlled trial. JMIR Res Protoc, 4:e45.

Yapay Zeka ve Psikoterapi

Yıl 2025, Cilt: 17 Sayı: 4, 643 - 660

Öz

Gelişen yapay zeka teknolojileri ile psikoterapi alanında da değişimler görülmeye başlamıştır. Yapay zeka, terapi alanında şu anda büyük bir etkiye sahip olmasa da, gelecekte terapinin niteliği ve insanlar ile terapistler arasındaki ilişkinin değeri konusunda büyük sorular ortaya çıkartmaktadır. Yapay zekanın terapi sürecine nasıl dahil edilebileceğini anlamaya çalışmak geleceği öngörmek, proaktif olmak önem kazanmaktadır. Bu makalede psikoterapi alanlarında, kullanılan mevcut yapay zeka uygulamaları literatür taraması ile irdelenecektir. Psikoterapinin etkinliğini artırmak için yapay zekanın kullanılabilebilmektedir. Ancak yapay zekaya aşırı güvenmenin psikoterapinin insan yönünü gölgeleyebileceği ve insan faktörünün önemli olduğunun unutulmaması gerekmektedir. Psikoterapide yapay zeka kullanımında mesleğin nasıl etkileneceği ve terapi süreçlerine nasıl dahil edileceği konusunda hala belirsizlikler olmakla birlikte yapay zekanın psikoterapide çok yönlü bir rol oynayabileceği öngörülmektedir.

Kaynakça

  • Abd-Alrazaq AA, Rababeh A, Alajlani M, Bewick BM, Househ M (2020) Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis. J Med Internet Res, 22:e16021.
  • Abd-Alrazaq A, AlSaad R, Harfouche M, Aziz S, Ahmed A, Damseh R. et al. (2023) Wearable artificial intelligence for detecting anxiety: systematic review and meta-analysis. J Med Internet Res, 25:e48754.
  • Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F (2022) Investigating the feasibility of assessing depression severity and valence-arousal with wearable sensors using discrete wavelet transforms and machine learning. Information, 13:406.
  • Andrews G, Issakidis C, Carter G. (2001) Shortfall in mental health service utilisation. Br J Psychiatry, 179:417-425.
  • Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 145:137-165.
  • Ashar YK, Clark J, Gunning FM, Goldin P, Gross JJ, Wager TD (2021) Brain markers predicting response to cognitive‐behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015. Transl Psychiatry, 11:260.
  • Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J et al. (2017) 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, 5:e18.
  • Bălan O, Moise G, Moldoveanu A, Leordeanu M, Moldoveanu F (2020) An investigation of various machine and deep learning techniques applied in automatic fear level detection and acrophobia virtual therapy. Sensors, 20:496.
  • Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela, JP (2018) Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology, 43:1660-1666.
  • Bayramlı I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW et al. (2022) Predictive structured-unstructured interactions in EHR models: A case study of suicide prediction. NPJ Digit Med, 5:15.
  • Bendig E, Erb B, Schulze-Thuesing L, Baumeister H (2022) The next generation: chatbots in clinical psychology and psychotherapy to foster mental health–a scoping review. Verhaltenstherapie, 32:64-76.
  • Bickman L (2020) Improving mental health services: A 50-year journey from randomized experiments to artificial intelligence and precision mental health. Adm Policy Ment Health, 47:795-843.
  • Birk RH, Samuel G (2022) Digital phenotyping for mental health: reviewing the challenges of using data to monitor and predict mental health problems. Curr Psychiatry Rep, 24:523-528.
  • Biswas A, Talukdar W (2024) Intelligent clinical documentation: harnessing generative AI for patientcentric clinical note generation. Int J Innov Sci Res Technol, 9:994-1008.
  • Blader SL, Rothman NB (2014) Paving the road to preferential treatment with good intentions: Empathy, accountability and fairness. J Exp Soc Psychol, 50:65-81.
  • Braga A, Logan RK (2017) The emperor of strong AI has no clothes: limits to artificial intelligence. Information, 8:156.
  • Bzdok D, Meyer-Lindenberg A (2018) Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging, 3:223-230.
  • Calderita LV, Manso LJ, Bustos P, Suárez CM, Fernández F, Bandera A (2014) Therapist: towards an autonomous socially interactive robot for motor and neurorehabilitation therapies for children. JMIR Rehabil Assist Technol, 1:e1.
  • Chekroud AM, Hawrilenko M, Loho H, Bondar J, Gueorguieva R, Hasan A et al. (2024) Illusory generalizability of clinical prediction models. Sci, 383:164-167.
  • Chen J, Li Y, Wu X, Liang Y, Jha S (2020) Robust out-of-distribution detection for neural networks. arXiv:2003.09711v6.
  • Coghlan S, Leins K, Sheldrick S, Cheong M, Gooding P, D'Alfonso S (2023) To chat or bot to chat: ethical issues with using chatbots in mental health. Digit Health, 9:20552076231183542.
  • Coiera E, Liu S (2022) Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med, 3:100860.
  • Craig TK, Rus-Calafell M, Ward T, Leff JP, Huckvale M, Howarth E et al. (2018) AVATAR therapy for auditory verbal hallucinations in people with psychosis: a single-blind, randomised controlled trial. Lancet Psychiatry, 5:31-40.
  • Crawford K (2021) Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Connecticut, Yale University Press.
  • Cuff BM, Brown SJ, Taylor L, Howat DJ (2016) Empathy: a review of the concept. Emotion, 8:144-153.
  • Cuthbert BN (2020) The role of RDoC in future classification of mental disorders. Dialogues Clin Neurosci, 22:81-85.
  • D’Alfonso S (2020) AI in mental health. Curr Opin Psychol, 36:112–117.
  • Dellazizzo L, Percie du Sert O, Phraxayavong K, Potvin S, O'Connor K, Dumais A (2018) Exploration of the dialogue components in Avatar Therapy for schizophrenia patients with refractory auditory hallucinations: a content analysis. Clin Psychol Psychother, 25:878-885.
  • Dogrucu A, Perucic A, Isaro A, Ball D, Toto E, Rundensteiner EA et al. (2020) Moodable: on feasibility of instantaneous depression assessment using machine learning on voice samples artificial intelligence, machine learning and mental healthcare 29 with retrospectively harvested smartphone and social media data. Smart Health, 17:100118.
  • Dong Y, Hou J, Zhang N, Zhang M (2020) Research on how human intelligence, consciousness, and cognitive computing affect the development of Artificial Intelligence. Complexity, 2020:1680845.
  • du Sert, OP, Potvin S, Lipp O, Dellazizzo L, Laurelli M, Breton R et al. (2018) Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: a pilot clinical trial. Schizophr Res, 97:176-181.
  • Dwyer DB, Falkai P, Koutsouleris N (2018) Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol, 14:91-118.
  • Eisenstadt M, Liverpool S, Infanti E, Ciuvat RM, Carlsson C (2021) Mobile apps that promote emotion regulation, positive mental health, and well-being in the general population: systematic review and meta-analysis. JMIR Ment Health, 8:e31170.
  • Farahany NA (2023) The Battle for Your Brain. New York, St. Martin’s Press.
  • Fiske A, Henningsen P, Buyx A (2019) Your robot therapist will see you now: ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. J Med Internet Res, 21:e13216.
  • Fitzpatrick KK, Darcy A, Vierhile M (2017) 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, 4:e19.
  • Freeman D, Haselton P, Freeman J, Spanlang B, Kishore S, Albery E et al. (2018) Automated psychological therapy using immersive virtual reality for treatment of fear of heights: a single-blind, parallel-group, randomised controlled trial. Lancet Psychiatry, 5:625-632.
  • Giguère S, Potvin S, Beaudoin M, Dellazizzo L, Giguère CÉ, Furtos A et al. (2023) Avatar intervention for cannabis use disorder in ındividuals with severe mental disorders: A pilot study. J Pers Med, 13:766.
  • Góngora SA, Hamrioui S, de la Torre Díez, I., Motta Cruz E, López MC, Franco M (2019) Social robots for people with aging and dementia: A systematic review of literature. Telemed J E Health, 25:533-540.
  • Gual-Montolio P, Jaén I, Martínez-Borba V, Castilla D, Suso-Ribera C (2022) Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real or close to real-time: A systematic review. Int J Environ Res Public Health, 19:7737.
  • He Y, Yang L, Qian C, Li T, Su Z, Zhang Q et al. (2023) Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res, 25:e43862.
  • Holohan M, Fiske A (2021) 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, 12:720476.
  • 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, 6:e12106.
  • Islam MR, Sakib MKH, Ulhaq A, Akter S, Zhou J, Asirvathamt D (2023) Sidvis: designing visual interactive system for analyzing suicide ideation detection. 2023 27th International Conference Information Visualisation (IV), Tampere, Finland, 2023, 384-389.
  • Kessler RC, Aguilar-Gaxiola S, Alonso J, Chatterji S, Lee S, Ormel J et al. (2009) The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys. Epidemiol Psychiatr Sci, 18:23-33.
  • Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R et al. (2021) Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry, 78:195-209.
  • Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. (2022) From promise to practice: Towards the realisation of AI-informed mental health care. Lancet, 4:e829-e840.
  • Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann, S, Rosen M, Ruef A, Dwyer DB et al. (2018) Prediction models of functional outcomes for individuals in the clinical high-risk state for psychosis or with recent-onset depression: a multimodal, multisite machine learning analysis. JAMA Psychiatry, 75:1156-1172
  • Lambert MJ (2004) Bergin and Garfield’s Handbook of Psychotherapy and Behavior Change. New York, Wiley.
  • Lamichhane B, Moukaddam N, Sabharwal A. (2024) Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Sci Rep, 14:18808.
  • Li H, Zhang R, Lee YC, Kraut RE, Mohr DC (2023) Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and wellbeing. NPJ Digit Med, 6:236-236.
  • Liu YS, Chokka S, Cao B, Chokka PR (2021a) Screening for bipolar disorder in a tertiary mental health centre using EarlyDetect: a machine learning-based pilot study. J Affect Disord Rep, 6:100215.
  • Liu Y, Hankey J, Cao B, Chokka P (2021b) Screening for major depressive disorder in a tertiary mental health centre using EarlyDetect: a machine learning-based pilot study. J Affect Disord Rep, 3:100062.
  • Lucas GM, Rizzo A, Gratch J, Scherer S, Stratou G, Boberg J et al. (2017) Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers. Front Robot AI, 4:51.
  • Luxton DD (2014) Artificial intelligence in psychological practice: current and future applications an implications. Prof Psychol Res Pr, 45:332-339.
  • Martin NM, Kreitmair K (2018) Ethical issues for direct-to-consumer digital psychotherapy apps: addressing accountability, data protection, and consent. JMIR Ment Health, 5:e32.
  • Martinez-Martin N, Kreitmair K (2018) Ethical issues for direct-to-consumer digital psychotherapy apps: addressing accountability, data protection, and consent. JMIR Ment Health, 5:e9423.
  • Mehrotra S, Kumar S, Sudhir P, Rao GN, Thirthalli J, Gandotra A (2017) Unguided mental health self-help apps: reflections on challenges through a clinician's lens. Indian J Psychol Med, 39:707-711.
  • Meyerhoff J, Liu T, Kording KP, Ungar LH, Kaiser SM, Karr CJ et al. (2021) Evaluation of changes in depression, anxiety, and social anxiety using smartphone sensor features: longitudinal cohort study. J Med Internet Res, 23:e22844.
  • Minerva F, Giubilini A (2023) Is AI the future of mental healthcare? Topoi, 42:809-817.
  • Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F (2023) Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: a systematic literature review. J Biomed Inform, 138:104278.
  • Nathan PE, Gorman JM (2015) A Guide to Treatments That Work. Oxford, Oxford University Press.
  • Nemesure MD, Heinz MV, Huang R, Jacobson NC (2021) Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep, 11:1980.
  • Olawade DB, Wada OZ, Odetayo A, David-Olawade AC, Asaolu F, Eberhardt J (2024) Enhancing mental health with artificial intelligence: current trends and future prospects. J Med Surg Public Health, 3:100099.
  • Oyebode O, Alqahtani F, Orji R (2020) Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access, 8:111141-111158.
  • Raamkumar AS, Yang Y (2022) Empathetic conversational systems: a review of current advances, gaps, and opportunities. IEEE Trans Affect Comput, 14:2722-2739.
  • Reece AG, Danforth CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Sci, 6:15.
  • Rein BA, McNeil DW, Hayes AR, Hawkins TA, Ng H. Mei, Yura CA (2018) Evaluation of an avatar-based training program to promote suicide prevention awareness in a college setting. J Am Coll Health, 66:401-411.
  • Richards D (2024) Artificial intelligence and psychotherapy: a counterpoint. Couns Psychother Res, https://doi.org/10.1002/capr.12758
  • Richter T, Fishbain B, Fruchter E, Richter-Levin G, Okon-Singer H (2021) Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. J Psychiatr Res, 141:199-205.
  • Roa TM, Biller-Andorno N, Trachsel M (2021) The ethics of artificial intelligence in psychotherapy. In The Oxford Handbook of Psychotherapy Ethics. (Eds M Trachsel, J Gaab, N Biller-Andorno, Ş Tekin, JZ Sadler):744-758. Oxford, Oxford University Press.
  • Roberts LW, Chan S, Torous J (2018) New tests, new tools: mobile and connected technologies in advancing psychiatric diagnosis. NPJ Digit Med, 1:20176.
  • Rosellini AJ, Liu S, Anderson GN, Sbi S, Tung ES, Knyazhanskaya E (2020) Developing algorithms to predict adult onset internalizing disorders: an ensemble learning approach. J Psychiatr Res, 121:189-196.
  • Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA (2020) A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med, 3:1-12.
  • Rozek DC, Andres WC, Smith NB, Leifker FR, Arne K, Jennings G et al. (2020) Using machine learning to predict suicide attempts in military personnel. Psychiatry Res, 294:113515.
  • Sachan D (2018) Self-help robots drive blues away. Lancet Psychiatry, 5:547.
  • Sajjadian M, Lam RW, Milev R, Rotzinger S, Frey BN, Soares CN et al. (2021) Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis. Psychol Med, 51:2742-2751.
  • Schwartz B, Cohen, ZD, Rubel JA, Zimmermann D, Wittmann WW, Lutz W (2021) Personalized treatment selection in routine care: integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychother Res, 31:33-51.
  • Shah J, DePietro B, D'Adamo L, Firebaugh ML, Laing O, Fowler LA et al. (2022) Development and usability testing of a chatbot to promote mental health services use among individuals with eating disorders following screening. Int J Eat Disord, 55:1229-1244.
  • Shatte AB, Hutchinson D, Teague SJ. (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med, 49:1426-1448.
  • Torjesen I. (2017) Sixty seconds on . . . sex with robots. Br Med J, 358:j3353.
  • Sönmez D, Hocaoğlu C. (2024) Metaverse and psychiatry: a review. Psikiyatride Güncel Yaklaşımlar, 16:225-238.
  • Srinivasan R, González BSM (2022) The role of empathy for artificial intelligence accountability. J Responsib Technol, 9:100021.
  • Stade EC, Stirman, SW, Ungar LH, Boland CL, Schwartz HA, Yaden DB et al. (2024) Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. Npj Ment Health Res, 3:12.
  • Roa TM, Biller-Andorno N, Trachsel M (2021) The ethics of artificial intelligence in psychotherapy. In The Oxford Handbook of Psychotherapy Ethics. (Eds M Trachsel, J Gaab, N Biller-Andorno, Ş Tekin, JZ Sadler):744-758. Oxford, Oxford University Press.
  • Stark L, Hoey J. (2021) The ethics of emotion in artificial intelligence systems. In FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency: 782-793. New York, Association for Computing Machinery. New York, United States.
  • Su C, Aseltine R, Doshi R, Chen K, Rogers SC, Wang F. (2020) Machine learning for suicide risk prediction in children and adolescents with electronic health records. Transl Psychiatry, 10:413.
  • Swaminathan A, López I, Mar RAG, Heist T, McClintock T, Caoili K et al. (2023) Natural language processing system for rapid detection and intervention of mental health crisis chat messages. NPJ Med, 6:213.
  • Taliaz D, Spinrad A, Barzilay R, Barnett-Itzhaki Z, Averbuch D, Teltsh O et al. (2021) Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry, 11:381.
  • Teding van Berkhout E, Malouff JM (2016) The efficacy of empathy training: a meta-analysis of randomized controlled trials. J Couns Psychol, 63:32-41.
  • Thakkar A, Gupta A, De Sousa A. (2024) Artificial intelligence in positive mental health: a narrative review. Front Digit Health, 6:1280235.
  • Turing AM (1950) Computing machinery and intelligence. Mind, 59:433-460.
  • Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I et al. (2023) An AI-based decision support system for predicting mental health disorders. Inf Syst Front, 25:1261-1276.
  • van den Bosch K, Bronkhorst K (2019) Six challenges for human-AI co-learning. Adaptive Instructional Systems, 11597:572–589.
  • Wada K, Shibata T (2007) Living with seal robots—its sociopsychological and physiological influences on the elderly at a care house. IEEE Trans Robot, 23:972-980.
  • Wang L, Fagan C, Yu CI (2020) Popular mental health apps (MH apps) as a complement to telepsychotherapy: guidelines for consideration. J Psychother Integr, 30:265-273.
  • Ward T, Rus-Calafell M, Ramadhan Z, Soumelidou O, Fornells-Ambrojo M, Garety P et al. (2020) AVATAR therapy for distressing voices: a comprehensive account of therapeutic targets. Schizophr Bull, 46:1038-1044.
  • Weizenbaum J. (1976) Computer Power and Human Reason: From Judgment to Calculation. London, Freeman.
  • Wenzel J, Haas SS, Dwyer DB, Ruef A, Oeztuerk OF, Antonucci LA. et al. (2021) Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints? Neuropsychopharmacology, 46:1475-1483.
  • Wiebe A, Aslan B, Brockmann C, Lepartz A, Dudek D, Kannen K et al. (2023) Multimodal assessment of adult attention‐deficit hyperactivity disorder: a controlled virtual seminar room study. Clin Psychol Psychother, 30:1111-1129.
  • Wong WH (2023) We, The Data: Human Rights in The Digital Age. Massachusetts, MIT Press.
  • Yu R, Hui E, Lee J, Poon D, Ng A, Sit K et al. (2015) Use of a therapeutic, socially assistive pet robot (PARO) in improving mood and stimulating social interaction and communication for people with dementia: study protocol for a randomized controlled trial. JMIR Res Protoc, 4:e45.
Toplam 102 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klinik Psikoloji
Bölüm Derleme
Yazarlar

Fatih Bal 0000-0002-9974-2033

Yayımlanma Tarihi
Gönderilme Tarihi 25 Ağustos 2024
Kabul Tarihi 29 Aralık 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 17 Sayı: 4

Kaynak Göster

AMA Bal F. Artificial Intelligence and Psychotherapy. Psikiyatride Güncel Yaklaşımlar. 17(4):643-660.

Creative Commons Lisansı
Psikiyatride Güncel Yaklaşımlar Creative Commons Atıf-Gayriticari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.