Machine Learning Applications in Schizophrenia: A Comprehensive Review
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Schizophrenia is a severe and heterogeneous neuropsychiatric disorder characterised by complex symptoms, uncertain aetiology, and variable treatment outcomes. Traditional diagnostic methods relying on clinical observation and self-report often fail to capture the underlying biological diversity of the illness. Recent advances in machine learning (ML) have introduced powerful tools for analysing multimodal data and improving diagnostic precision, risk prediction, and treatment outcomes in schizophrenia. This comprehensive review summarises studies published between 2010 and 2025 that applied ML methods to schizophrenia across diverse data modalities, including neuroimaging, genomics, electronic health records, cognitive assessments, and digital phenotyping. Evidence shows that ML models, particularly deep learning and multimodal fusion techniques, can effectively distinguish schizophrenia from other psychiatric conditions, identify individuals at ultra-high risk for psychosis, predict treatment response, and uncover biologically meaningful subtypes. Despite these advances, major challenges remain, including small and imbalanced datasets, limited generalisability, model opacity, and ethical concerns related to privacy and bias. Addressing these limitations through large-scale, diverse datasets, explainable AI, and ethical frameworks will be essential for clinical translation. Integrating ML into psychiatric decision-support systems may enable earlier diagnosis, personalised treatment, and better long-term outcomes. With continued development and responsible implementation, ML holds the potential to transform schizophrenia care and advance the realisation of precision psychiatry.Abstract
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Patel KR, Cherian J, Gohil K, Atkinson D. Schizophrenia: Overview and Treatment Options. Pharmacy and Therapeutics 2014;39:638. [2] Luvsannyam E, Jain MS, Pormento MKL, Siddiqui H, Balagtas ARA, Emuze BO, et al. Neurobiology of Schizophrenia: A Comprehensive Review. Cureus 2022;14:e23959. https://doi.org/10.7759/CUREUS.23959. [3] Insel TR, Cuthbert BN. Brain disorders? Precisely: Precision medicine comes to psychiatry. Science (1979) 2015;348:499–500. https://doi.org/10.1126/SCIENCE.AAB2358. [4] Chen ZS, Kulkarni P (Param), Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3:100602. https://doi.org/10.1016/J.PATTER.2022.100602. [5] Reynolds T, Johnson EC, Huggett SB, Bubier JA, Palmer RHC, Agrawal A, et al. Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration. Neuropsychopharmacology 2021;46:86–97. https://doi.org/10.1038/S41386-020-00795-5. [6] Lydon-Staley DM, Cornblath EJ, Blevins AS, Bassett DS. Modeling brain, symptom, and behavior in the winds of change. Neuropsychopharmacology 2021;46:20–32. https://doi.org/10.1038/S41386-020-00805-6. [7] von Ziegler L, Sturman O, Bohacek J. Big behavior: challenges and opportunities in a new era of deep behavior profiling. Neuropsychopharmacology 2021;46:33–44. https://doi.org/10.1038/S41386-020-0751-7. [8] Onnela JP. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology 2021;46:45–54. https://doi.org/10.1038/S41386-020-0771-3. [9] Ressler KJ, Williams LM. Big data in psychiatry: multiomics, neuroimaging, computational modeling, and digital phenotyping. Neuropsychopharmacology 2020;46:1. https://doi.org/10.1038/S41386-020-00862-X. [10] Feczko E, Miranda-Dominguez O, Marr M, Graham AM, Nigg JT, Fair DA. The Heterogeneity problem: Approaches to identify psychiatric subtypes. Trends Cogn Sci 2019;23:584. https://doi.org/10.1016/J.TICS.2019.03.009. [11] Gao CX, Dwyer D, Zhu Y, Smith CL, Du L, Filia KM, et al. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res 2023;327:115265. https://doi.org/10.1016/J.PSYCHRES.2023.115265. [12] Narendra Kumar Rao B, Hemanth Raga Sai P, Naseeba B, Venkata Phani Karthik B, Madhavi G. Multilingual Text Identification Using NLP and Machine Learning 2024:29–36. https://doi.org/10.1007/978-981-99-2832-3_5. [13] Mangla P, Singh G, Pathak N, Chawla S. Language Identification Using Multinomial Naive Bayes Technique. Lecture Notes in Networks and Systems 2024;786:307–16. https://doi.org/10.1007/978-981-99-6547-2_24. [14] Thu YK, Aung T, Supnithi T. Neural Sequence Labeling Based Sentence Segmentation for Myanmar Language. Lecture Notes in Networks and Systems 2023;734 LNNS:285–96. https://doi.org/10.1007/978-3-031-36886-8_24. [15] Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science (1979) 2015;349:255–60. https://doi.org/10.1126/SCIENCE.AAA8415. [16] Walter M, Alizadeh S, Jamalabadi H, Lueken U, Dannlowski U, Walter H, et al. Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry 2019;91:113–21. https://doi.org/10.1016/J.PNPBP.2018.09.014. [17] Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020;88:818–28. https://doi.org/10.1016/J.BIOPSYCH.2020.02.016. [18] Eyre HA, Singh AB, Reynolds C. Tech giants enter mental health. World Psychiatry 2016;15:21–2. https://doi.org/10.1002/WPS.20297. [19] Rutherford S. The Promise of Machine Learning for Psychiatry. Biol Psychiatry 2020;88:e53–5. https://doi.org/10.1016/J.BIOPSYCH.2020.08.024. [20] Chen ZS, Kulkarni P (Param), Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3:100602. https://doi.org/10.1016/J.PATTER.2022.100602. [21] Li G, Han D, Wang C, Hu W, Calhoun VD, Wang YP. Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. Comput Methods Programs Biomed 2020;183. https://doi.org/10.1016/J.CMPB.2019.105073. [22] Aslan MF, Unlersen MF, Sabanci K, Durdu A. CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Appl Soft Comput 2021;98. https://doi.org/10.1016/J.ASOC.2020.106912. [23] Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng 2021;18. https://doi.org/10.1088/1741-2552/abc902. [24] Al-Qatf M, Lasheng Y, Al-Habib M, Al-Sabahi K. Deep Learning Approach Combining Sparse Autoencoder with SVM for Network Intrusion Detection. IEEE Access 2018;6:52843–56. https://doi.org/10.1109/ACCESS.2018.2869577. [25] Zhang X, Yao L, Huang C, Wang S, Tan M, Long G, et al. Multi-modality sensor data classification with selective attention. IJCAI International Joint Conference on Artificial Intelligence 2018;2018-July:3111–7. https://doi.org/10.24963/ijcai.2018/432. [26] Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference 2014:1724–34. https://doi.org/10.3115/v1/d14-1179. [27] Minaee S, Azimi E, Abdolrashidi A. Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models 2019. [28] Shankar D, Narumanchi S, Ananya HA, Kompalli P, Chaudhury K. Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce 2017. [29] Kingma DP, Welling M. Auto-encoding variational bayes. 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings 2014. https://doi.org/10.61603/ceas.v2i1.33. [30] Kingma DP, Welling M. An introduction to variational autoencoders. Foundations and Trends in Machine Learning 2019;12:307–92. https://doi.org/10.1561/2200000056. [31] Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput Sci 2021;2:420. https://doi.org/10.1007/S42979-021-00815-1. [32] Pugliese R, Regondi S, Marini R. Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Science and Management 2021;4:19–29. https://doi.org/10.1016/J.DSM.2021.12.002. [33] Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2021;2:160. https://doi.org/10.1007/S42979-021-00592-X. [34] Gashkarimov VR, Sultanova RI, Efremov IS, Asadullin A. Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review. Consortium Psychiatricum 2023;4:43. https://doi.org/10.17816/CP11030. [35] Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, et al. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023;21:241. https://doi.org/10.1186/S12916-023-02941-4. [36] Chen ZS, Kulkarni P (Param), Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3:100602. https://doi.org/10.1016/J.PATTER.2022.100602. [37] Ebrahimzadeh E, Saharkhiz S, Rajabion L, Oskouei HB, Seraji M, Fayaz F, et al. Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function. Front Syst Neurosci 2022;16. https://doi.org/10.3389/FNSYS.2022.934266. [38] Bringas-Vega ML, Michel CM, Saxena S, White T, Valdes-Sosa PA. Neuroimaging and global health. Neuroimage 2022;260. https://doi.org/10.1016/J.NEUROIMAGE.2022.119458. [39] Morita T, Asada M, Naito E. Contribution of neuroimaging studies to understanding development of human cognitive brain functions. Front Hum Neurosci 2016;10. https://doi.org/10.3389/FNHUM.2016.00464. [40] Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life 2023;13:1472. https://doi.org/10.3390/LIFE13071472. [41] Sepede G, De Berardis D, Campanella D, Perrucci MG, Ferretti A, Salerno RM, et al. Neural correlates of negative emotion processing in bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2015;60:1–10. https://doi.org/10.1016/J.PNPBP.2015.01.016. [42] Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life 2023;13:1472. https://doi.org/10.3390/LIFE13071472. [43] Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR. Deep convolutional neural network model for automated diagnosis of Schizophrenia using EEG signals. Applied Sciences (Switzerland) 2019;9. https://doi.org/10.3390/APP9142870. [44] Vita A, De Peri L, Deste G, Sacchetti E. Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry 2012;2:e190. https://doi.org/10.1038/TP.2012.116. [45] Zhang Y, Catts VS, Sheedy D, McCrossin T, Kril JJ, Shannon Weickert C. Cortical grey matter volume reduction in people with schizophrenia is associated with neuro-inflammation. Transl Psychiatry 2016;6:e982. https://doi.org/10.1038/TP.2016.238. [46] Aksoy G, Cattan G, Chakraborty S, Karabatak M. Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records. J Med Syst 2024;48. https://doi.org/10.1007/s10916-024-02048-0. [47] Zhao Z, Deng Y, Zhang Y, Zhang Y, Zhang X, Shao L. DeepFHR: Intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BMC Med Inform Decis Mak 2019;19. https://doi.org/10.1186/s12911-019-1007-5. [48] Mortier S, Turkeš R, De Winne J, Van Ransbeeck W, Botteldooren D, Devos P, et al. Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography. Sensors 2023;23. https://doi.org/10.3390/s23239588. [49] Bergen SE, Petryshen TL. Genome-wide association studies (GWAS) of schizophrenia: does bigger lead to better results? Curr Opin Psychiatry 2012;25:76. https://doi.org/10.1097/YCO.0B013E32835035DD. [50] Bracher-Smith M, Rees E, Menzies G, Walters JTR, O’Donovan MC, Owen MJ, et al. Machine learning for prediction of schizophrenia using genetic and demographic factors in the UK biobank. Schizophr Res 2022;246:156. https://doi.org/10.1016/J.SCHRES.2022.06.006. [51] Coors A, Imtiaz MA, Boenniger MM, Aziz NA, Breteler MMB, Ettinger U. Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes. Psychol Med 2021;53:1611. https://doi.org/10.1017/S0033291721003251. [52] Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010;42:565–9. https://doi.org/10.1038/NG.608. [53] Zhao Z, Dorn S, Wu Y, Yang X, Jin J, Lu Q. One score to rule them all: regularized ensemble polygenic risk prediction with GWAS summary statistics. BioRxiv 2024:2024.11.27.625748. https://doi.org/10.1101/2024.11.27.625748. [54] Fong WJ, Tan HM, Garg R, Teh AL, Pan H, Gupta V, et al. Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation. Front Neuroinform 2023;17:1244336. https://doi.org/10.3389/FNINF.2023.1244336/FULL. [55] Swinckels L, Bennis FC, Ziesemer KA, Scheerman JFM, Bijwaard H, de Keijzer A, et al. The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review. J Med Internet Res 2024;26. https://doi.org/10.2196/48320. [56] hua yining, Blackley S, Shinn A, Skinner J, Moran L, Zhou L. Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models. Res Sq 2024:rs.3.rs-4126574. https://doi.org/10.21203/RS.3.RS-4126574/V1. [57] Garriga R, Buda TS, Guerreiro J, Omaña Iglesias J, Estella Aguerri I, Matić A. Combining clinical notes with structured electronic health records enhances the prediction of mental health crises. Cell Rep Med 2023;4:101260. https://doi.org/10.1016/J.XCRM.2023.101260. [58] Gashkarimov VR, Sultanova RI, Efremov IS, Asadullin A. Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review. Consortium Psychiatricum 2023;4:43. https://doi.org/10.17816/CP11030. [59] Chivilgina O, Elger BS, Jotterand F. Digital Technologies for Schizophrenia Management: A Descriptive Review. Sci Eng Ethics 2021;27. https://doi.org/10.1007/S11948-021-00302-Z. [60] Taipale H, Schneider-Thoma J, Pinzón-Espinosa J, Radua J, Efthimiou O, Vinkers CH, et al. Representation and Outcomes of Individuals with Schizophrenia Seen in Everyday Practice Who Are Ineligible for Randomized Clinical Trials. JAMA Psychiatry 2022;79:210–8. https://doi.org/10.1001/JAMAPSYCHIATRY.2021.3990. [61] Birk RH, Samuel G. Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems. Curr Psychiatry Rep 2022;24:523–8. https://doi.org/10.1007/S11920-022-01358-9. [62] Chukka A, Choudhary S, Dutt S, Bondre A, Reddy P, Tugnawat D, et al. Digital Interventions for Relapse Prevention, Illness Self-Management, and Health Promotion In Schizophrenia: Recent Advances, Continued Challenges, and Future Opportunities. Curr Treat Options Psychiatry 2023;10:346–71. https://doi.org/10.1007/S40501-023-00309-2. [63] Lane E, D’Arcey J, Kidd S, Onyeaka H, Alon N, Joshi D, et al. Digital Phenotyping in Adults with Schizophrenia: A Narrative Review. Curr Psychiatry Rep 2023;25:699–706. https://doi.org/10.1007/S11920-023-01467-Z/FIGURES/1. [64] Yan W, Zhao M, Fu Z, Pearlson GD, Sui J, Calhoun VD. Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series. Schizophr Res 2021;245:141. https://doi.org/10.1016/J.SCHRES.2021.02.007. [65] Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, et al. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Curr Psychiatry Rep 2022;24:925. https://doi.org/10.1007/S11920-022-01399-0. [66] Ostojic D, Lalousis PA, Donohoe G, Morris DW. The challenges of using machine learning models in psychiatric research and clinical practice. European Neuropsychopharmacology 2024;88:53–65. https://doi.org/10.1016/J.EURONEURO.2024.08.005. [67] Sun J, Lu T, Shao X, Han Y, Xia Y, Zheng Y, et al. Practical AI application in psychiatry: historical review and future directions. Mol Psychiatry 2025;30:4399. https://doi.org/10.1038/S41380-025-03072-3. [68] Yassin W, Nakatani H, Zhu Y, Kojima M, Owada K, Kuwabara H, et al. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry 2020;10:1–11. https://doi.org/10.1038/S41398-020-00965-5;TECHMETA. [69] Deneault A, Dumais A, Désilets M, Hudon A. Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges. J Pers Med 2024;14:744. https://doi.org/10.3390/JPM14070744/S1. [70] De Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, et al. Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr Dis Treat 2019;15:1605. https://doi.org/10.2147/NDT.S202418. [71] Cao P, Li R, Li Y, Dong Y, Tang Y, Xu G, et al. Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging. J Affect Disord 2025;383:20–31. https://doi.org/10.1016/J.JAD.2025.04.135. [72] 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;26:493. https://doi.org/10.1097/YCO.0B013E3283642DFD. [73] Ciapparelli A, Dell’Osso L, Di Poggio AB, Carmassi C, Cecconi D, Fenzi M, et al. Clozapine in treatment-resistant patients with schizophrenia, schizoaffective disorder, or psychotic bipolar disorder: A naturalistic 48-month follow-up study. Journal of Clinical Psychiatry 2003;64:451–8. https://doi.org/10.4088/JCP.v64n0416. [74] Fonseca de Freitas D, Kadra-Scalzo G, Agbedjro D, Francis E, Ridler I, Pritchard M, et al. Using a statistical learning approach to identify sociodemographic and clinical predictors of response to clozapine. J Psychopharmacol 2022;36:498–506. https://doi.org/10.1177/02698811221078746. [75] Del Fabro L, Bondi E, Serio F, Maggioni E, D’Agostino A, Brambilla P. Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023;13:75. https://doi.org/10.1038/S41398-023-02371-Z. [76] De Hert M, Schreurs V, Vancampfort D, Van Winkel R. Metabolic syndrome in people with schizophrenia: a review. World Psychiatry 2009;8:15. https://doi.org/10.1002/J.2051-5545.2009.TB00199.X. [77] Habehh H, Gohel S. Machine Learning in Healthcare. Curr Genomics 2021;22:291–300. https://doi.org/10.2174/1389202922666210705124359. [78] Del Fabro L, Bondi E, Serio F, Maggioni E, D’Agostino A, Brambilla P. Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023;13:75. https://doi.org/10.1038/S41398-023-02371-Z. [79] Wang X, Zhang Y, Wang PJ, Yan Q, Wang XX, Wu HS, et al. Altered Brain Network Dynamics in Schizophrenia Patients With Predominant Negative Symptoms: A Resting‐State fMRI Study Using Co‐Activation Pattern Analysis. Hum Brain Mapp 2025;46:e70369. https://doi.org/10.1002/HBM.70369. [80] Ottet MC, Schaer M, Debbané M, Cammoun L, Thiran JP, Eliez S. Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations. Front Hum Neurosci 2013;7:402. https://doi.org/10.3389/FNHUM.2013.00402. [81] Mena N, Ab M. Machine Learning techniques and Polygenic Risk Score application to prediction genetic diseases. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2020;9:5–14. https://doi.org/10.14201/ADCAIJ202091514. [82] Guerrin CGJ, Doorduin J, Sommer IE, de Vries EFJ. The dual hit hypothesis of schizophrenia: Evidence from animal models. Neurosci Biobehav Rev 2021;131:1150–68. https://doi.org/10.1016/J.NEUBIOREV.2021.10.025. [83] Lysaker PH, Pattison ML, Leonhardt BL, Phelps S, Vohs JL. Insight in schizophrenia spectrum disorders: relationship with behavior, mood and perceived quality of life, underlying causes and emerging treatments. World Psychiatry 2018;17:12. https://doi.org/10.1002/WPS.20508. [84] Zonayed M, Tasnim R, Jhara SS, Mimona MA, Hussein MR, Mobarak MH, et al. Machine learning and IoT in healthcare: Recent advancements, challenges & future direction. Adv Biomark Sci Technol 2025;7:335–64. https://doi.org/10.1016/J.ABST.2025.08.006. [85] Chen ZS, Kulkarni P (Param), Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3:100602. https://doi.org/10.1016/J.PATTER.2022.100602. [86] Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2020;46:176. https://doi.org/10.1038/S41386-020-0767-Z. [87] Yasin S, Adeel M, Draz U, Ali T, Hijji M, Ayaz M, et al. A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals. Bioengineering 2025;12:641. https://doi.org/10.3390/BIOENGINEERING12060641. [88] Li C, Chen J, Dong M, Yan H, Chen F, Mao N, et al. Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application. BMC Psychiatry 2025;25:372. https://doi.org/10.1186/S12888-025-06817-0. [89] Cortes-Briones JA, Tapia-Rivas NI, D’Souza DC, Estevez PA. Going deep into schizophrenia with artificial intelligence. Schizophr Res 2022;245:122–40. https://doi.org/10.1016/J.SCHRES.2021.05.018. [90] Khan W, Daud A, Khan K, Muhammad S, Haq R. Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends. Natural Language Processing Journal 2023;4:100026. https://doi.org/10.1016/J.NLP.2023.100026. [91] Abbas Q, Jeong W, Lee SW. Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. Healthcare 2025;13:2154. https://doi.org/10.3390/HEALTHCARE13172154. [92] Muthukrishna M, Bell A V., Henrich J, Curtin CM, Gedranovich A, McInerney J, et al. Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) Psychology: Measuring and Mapping Scales of Cultural and Psychological Distance. Psychol Sci 2020;31:678–701. https://doi.org/10.1177/0956797620916782. [93] Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023;30:2050. https://doi.org/10.1093/JAMIA/OCAD180. [94] Timmons AC, Duong JB, Simo Fiallo N, Lee T, Vo HPQ, Ahle MW, et al. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspect Psychol Sci 2022;18:1062. https://doi.org/10.1177/17456916221134490. [95] Nouis SCE, Uren V, Jariwala S. Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK. BMC Med Ethics 2025;26:89. https://doi.org/10.1186/S12910-025-01243-Z. [96] Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 2020;3:118. https://doi.org/10.1038/S41746-020-00324-0. [97] Torous J, Linardon J, Goldberg SB, Sun S, Bell I, Nicholas J, et al. The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality. World Psychiatry 2025;24:156. https://doi.org/10.1002/WPS.21299. [98] Nouis SCE, Uren V, Jariwala S. Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK. BMC Med Ethics 2025;26:89. https://doi.org/10.1186/S12910-025-01243-Z.
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