Published February 16, 2025 | Version v1
Journal article Open

Inteligencia artificial en psiquiatría: innovaciones, desafíos y futuro del diagnóstico y tratamiento. Revisión bibliográfica

Authors/Creators

  • 1. Diplomado en Salud Ocupacional, Diplomado en Manejo de Situaciones Críticas en Salud Mental. Universidad de Los Andes. Chile.

Description

La inteligencia artificial (IA) se trata de un recurso tecnológico y revolucionario que se encuentra transformando la psiquiatría mediante el desarrollo de herramientas avanzadas que optimizan tanto el diagnóstico como el tratamiento de los trastornos mentales. Tecnologías como el aprendizaje automático (AA) y el procesamiento de lenguaje natural (PLN) permiten analizar patrones complejos en neuroimágenes, datos clínicos y lenguaje, facilitando diagnósticos más tempranos y personalizados. No obstante, la adopción de estas tecnologías enfrenta desafíos considerables, entre ellos las preocupaciones éticas relacionadas con la privacidad de los datos, los sesgos inherentes en los modelos y la falta de estándares que aseguren la reproducibilidad de los resultados. Basado en una revisión bibliográfica sistematizada de estudios recientes, se evaluaron las tecnologías de IA, incluyendo algoritmos de AA, PLN y análisis de neuroimágenes, con el objetivo de identificar su eficacia, limitaciones y posibilidades futuras. La evidencia indica que las herramientas predictivas logran tasas de precisión superiores al 90% en la detección de enfermedades como el Alzheimer y la depresión mayor, mientras que, sistemas como chatbots terapéuticos ofrecen apoyo continuo, anónimo y accesible a los pacientes. Aunque prometedora, la IA enfrenta barreras éticas, técnicas y sociales. Sin embargo, posee una potencial capacidad para atender la creciente demanda de servicios psiquiátricos, especialmente en regiones con acceso limitado a especialistas. En el futuro, estas tecnologías podrían evolucionar hacia sistemas más integrados y responsables, capaces de combinar avances tecnológicos con principios humanos como la empatía, promoviendo diagnósticos equitativos y tratamientos adaptados a las necesidades individuales.

Files

11 Inteligencia artificial en psiquiatría.pdf

Files (485.8 kB)

Name Size Download all
md5:8fc93eb235c073bea7e63c6fabfb384b
485.8 kB Preview Download

Additional details

References

  • Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. J Med Internet Res. 2023;25:e40789
  • Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–57.
  • Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–10.
  • Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep. 2019;21(11):116.
  • Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, et al. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open. 2023;6(3):e231671.
  • Rogge E, Briganti G. The Borders of Mental Illness: Investigating Clusters of Diagnostic Criteria in Acute Presentations of Psychiatric Disorders. Psychiatr Danub. 2022;34(Suppl 8):220–30.
  • Bzdok D, Meyer-Lindenberg A. Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–30.
  • Braun U, Schaefer A, Betzel RF, Tost H, Meyer-Lindenberg A, Bassett DS. From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron. 2018;97(1):14–31.
  • Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol. 2018;14:91–118.
  • Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artif Intell Med. 2020;102:101753.
  • Meinke C, Lueken U, Walter H, Hilbert K. Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2024;160:105640.
  • Briganti G. Artificial Intelligence in Psychiatry. Psychiatr Danub. 2023;35(Suppl 2):15–9.
  • Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24(11):1583–98.
  • Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
  • Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6.
  • Briganti G. Intelligence artificielle: une introduction pour les cliniciens. Revue des Maladies Respiratoires. 2023;40(4):308–13.
  • Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4.
  • Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, et al. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord. 2019;21(7):582–94.
  • Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Med Inform Decis Mak. 2023;23(1):271.
  • Park JH, Cho HE, Kim JH, Wall M, Stern Y, Lim H, et al. Machine Learning Prediction of Incidence of Alzheimer's Disease Using Large-Scale Administrative Health Data [Internet]. bioRxiv; 2020 [citado el 11 de noviembre de 2024]. p. 625582. Disponible en: https://www.biorxiv.org/content/10.1101/625582v2
  • Liu Y, Chen PHC, Krause J, Peng L. How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. JAMA. 2019;322(18):1806–16.
  • Lee EE, Torous J, Choudhury MD, Depp CA, Graham SA, Kim HC, et al. Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological psychiatry Cognitive neuroscience and neuroimaging. 2021;6(9):856.
  • Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc. 2019;26(4):364–79.
  • Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform. 2019;7(2):e12239.
  • McCoy TH, Pellegrini AM, Perlis RH. Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depress Anxiety. 2019;36(5):392–9.
  • Coppersmith G, Leary R, Crutchley P, Fine A. Natural Language Processing of Social Media as Screening for Suicide Risk. Biomedical Informatics Insights. 2018;10:1178222618792860.
  • Stewart R, Velupillai S. Applied natural language processing in mental health big data. Neuropsychopharmacology. 2021;46(1):252–3.
  • Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30(4):449–59.
  • Liu Z, Kainth K, Zhou A, Deyer TW, Fayad ZA, Greenspan H, et al. A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation. NMR Biomed. 2024;37(8):e5143.
  • Briganti G. A Doctor's Guide to Foundation Models [Internet]. OSF; 2023 [citado el 11 de noviembre de 2024]. Disponible en: https://osf.io/5zg3q
  • Shin H, Kang Y, Choi KW, Kim S, Ham BJ, Choi Y. Artificial Intelligence-Based Major Depressive Disorder (MDD) Diagnosis Using Raman Spectroscopic Features of Plasma Exosomes. Anal Chem. 2023;95(15):6410–6.
  • Noda Y, Sakaue K, Wada M, Takano M, Nakajima S. Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices. J Pers Med. 2024;14(1):101.
  • Allesøe RL, Nudel R, Thompson WK, Wang Y, Nordentoft M, Børglum AD, et al. Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression. Sci Adv. 2022;8(26):eabi7293.
  • Chan CC, Norel R, Agurto C, Lysaker PH, Myers EJ, Hazlett EA, et al. Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia. Schizophrenia Bulletin. 2023;49(2):444–53.
  • Fu J, Yang S, He F, He L, Li Y, Zhang J, et al. Sch-net: a deep learning architecture for automatic detection of schizophrenia. Biomed Eng Online. 2021;20(1):75.
  • Huang J, Zhao Y, Qu W, Tian Z, Tan Y, Wang Z, et al. Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network. Asian J Psychiatr. 2022;77:103263.
  • Doborjeh M, Doborjeh Z, Merkin A, Bahrami H, Sumich A, Krishnamurthi R, et al. Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia. Neural Netw. 2021;144:522–39.
  • James C, Ranson JM, Everson R, Llewellyn DJ. Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Netw Open. 2021;4(12):e2136553.
  • You J, Zhang YR, Wang HF, Yang M, Feng JF, Yu JT, et al. Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study. EClinicalMedicine. 2022;53:101665.
  • Salehi AW, Baglat P, Sharma BB, Gupta G, Upadhya A. A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI. En: 2020 International Conference on Smart Electronics and Communication (ICOSEC) [Internet]. 2020 [citado el 8 de enero de 2025]. p. 156–61. Disponible en: https://ieeexplore.ieee.org/document/9215402
  • Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. J Biomed Inform. 2023;137:104254.
  • Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, et al. Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. Lancet Digit Health. junio de 2022;4(6):e426–35.
  • Gomeni R, Bressolle-Gomeni F, Fava M. Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo. Transl Psychiatry. 2023;13(1):141.
  • Joyce JB, Grant CW, Liu D, MahmoudianDehkordi S, Kaddurah-Daouk R, Skime M, et al. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021;11(1):513.
  • Taliaz D, Spinrad A, Barzilay R, Barnett-Itzhaki Z, Averbuch D, Teltsh O, et al. Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021;11(1):381.
  • Sadeh-Sharvit S, Camp TD, Horton SE, Hefner JD, Berry JM, Grossman E, et al. Effects of an Artificial Intelligence Platform for Behavioral Interventions on Depression and Anxiety Symptoms: Randomized Clinical Trial. J Med Internet Res. 2023;25:e46781.
  • Wang M, Hu K, Fan L, Yan H, Li P, Jiang T, et al. Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score. Front Genet. 2022;13:848205.
  • Tandon R. Controversies in psychiatry practice and research. Asian J Psychiatr. 2023;81:103508.
  • Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195.
  • 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.
  • Castelvecchi D. Can we open the black box of AI? Nature. 2016;538(7623):20–3.
  • Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R. Artificial intelligence and Psychiatry: An overview. Asian J Psychiatr. 2022;70:103021.
  • Yew GCK. Trust in and Ethical Design of Carebots: The Case for Ethics of Care. Int J Soc Robot. 2021;13(4):629–45.
  • Blease C, Locher C, Leon-Carlyle M, Doraiswamy M. Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey. Digit Health. 2020;6:2055207620968355.
  • Tandon R. Computational psychiatry and the psychopathology of psychosis: Promising leads and blind alleys. Schizophr Res. 2023;254:143–5.
  • Riley P. Three pitfalls to avoid in machine learning. Nature. 2019;572(7767):27–9.
  • Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, et al. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med. 2022;5(1):171.
  • Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry. 2020;10(1):116.
  • Sodhani S, Chandar S, Bengio Y. Toward Training Recurrent Neural Networks for Lifelong Learning. Neural Comput. 2020;32(1):1–35.
  • Couckuyt A, Seurinck R, Emmaneel A, Quintelier K, Novak D, Van Gassen S, et al. Challenges in translational machine learning. Hum Genet. 2022;141(9):1451–66.
  • Zhong Y, Lyu YAH, Yu S, Gao YJ, Mi WF, Li JF. The issue of evidence-based medicine and artificial intelligence. Asian J Psychiatr. 2023;85:103627.
  • Hauser TU, Skvortsova V, De Choudhury M, Koutsouleris N. The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health. 2022;4(11):e816–28.
  • Low DM, Rumker L, Talkar T, Torous J, Cecchi G, Ghosh SS. Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. J Med Internet Res. 2020;22(10):e22635.
  • Thornton J, D'Souza R, Tandon R. Artificial intelligence and psychiatry research and practice. Asian J Psychiatr. 2023;81:103509.
  • 1. Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial Intelligence-Based Chatbots for Promoting Health Behavioral Changes: Systematic Review. J Med Internet Res. 2023;25:e40789. 2. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127–57. 3. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–10. 4. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep. 2019;21(11):116. 5. Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, et al. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open. 2023;6(3):e231671. 6. Rogge E, Briganti G. The Borders of Mental Illness: Investigating Clusters of Diagnostic Criteria in Acute Presentations of Psychiatric Disorders. Psychiatr Danub. 2022;34(Suppl 8):220–30. 7. Bzdok D, Meyer-Lindenberg A. Machine Learning for Precision Psychiatry: Opportunities and Challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3(3):223–30. 8. Braun U, Schaefer A, Betzel RF, Tost H, Meyer-Lindenberg A, Bassett DS. From Maps to Multi-dimensional Network Mechanisms of Mental Disorders. Neuron. 2018;97(1):14–31. 9. Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol. 2018;14:91–118. 10. Doraiswamy PM, Blease C, Bodner K. Artificial intelligence and the future of psychiatry: Insights from a global physician survey. Artif Intell Med. 2020;102:101753. 11. Meinke C, Lueken U, Walter H, Hilbert K. Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2024;160:105640. 12. Briganti G. Artificial Intelligence in Psychiatry. Psychiatr Danub. 2023;35(Suppl 2):15–9. 13. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019;24(11):1583–98. 14. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. 15. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6. 16. Briganti G. Intelligence artificielle: une introduction pour les cliniciens. Revue des Maladies Respiratoires. 2023;40(4):308–13. 17. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4. 18. Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, et al. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord. 2019;21(7):582–94. 19. Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Med Inform Decis Mak. 2023;23(1):271. 20. Park JH, Cho HE, Kim JH, Wall M, Stern Y, Lim H, et al. Machine Learning Prediction of Incidence of Alzheimer's Disease Using Large-Scale Administrative Health Data [Internet]. bioRxiv; 2020 [citado el 11 de noviembre de 2024]. p. 625582. Disponible en: https://www.biorxiv.org/content/10.1101/625582v2 21. Liu Y, Chen PHC, Krause J, Peng L. How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature. JAMA. 2019;322(18):1806–16. 22. Lee EE, Torous J, Choudhury MD, Depp CA, Graham SA, Kim HC, et al. Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological psychiatry Cognitive neuroscience and neuroimaging. 2021;6(9):856. 23. Koleck TA, Dreisbach C, Bourne PE, Bakken S. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J Am Med Inform Assoc. 2019;26(4):364–79. 24. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform. 2019;7(2):e12239. 25. McCoy TH, Pellegrini AM, Perlis RH. Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depress Anxiety. 2019;36(5):392–9. 26. Coppersmith G, Leary R, Crutchley P, Fine A. Natural Language Processing of Social Media as Screening for Suicide Risk. Biomedical Informatics Insights. 2018;10:1178222618792860. 27. Stewart R, Velupillai S. Applied natural language processing in mental health big data. Neuropsychopharmacology. 2021;46(1):252–3. 28. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30(4):449–59. 29. Liu Z, Kainth K, Zhou A, Deyer TW, Fayad ZA, Greenspan H, et al. A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation. NMR Biomed. 2024;37(8):e5143. 30. Briganti G. A Doctor's Guide to Foundation Models [Internet]. OSF; 2023 [citado el 11 de noviembre de 2024]. Disponible en: https://osf.io/5zg3q 31. Shin H, Kang Y, Choi KW, Kim S, Ham BJ, Choi Y. Artificial Intelligence-Based Major Depressive Disorder (MDD) Diagnosis Using Raman Spectroscopic Features of Plasma Exosomes. Anal Chem. 2023;95(15):6410–6. 32. Noda Y, Sakaue K, Wada M, Takano M, Nakajima S. Development of Artificial Intelligence for Determining Major Depressive Disorder Based on Resting-State EEG and Single-Pulse Transcranial Magnetic Stimulation-Evoked EEG Indices. J Pers Med. 2024;14(1):101. 33. Allesøe RL, Nudel R, Thompson WK, Wang Y, Nordentoft M, Børglum AD, et al. Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression. Sci Adv. 2022;8(26):eabi7293. 34. Chan CC, Norel R, Agurto C, Lysaker PH, Myers EJ, Hazlett EA, et al. Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia. Schizophrenia Bulletin. 2023;49(2):444–53. 35. Fu J, Yang S, He F, He L, Li Y, Zhang J, et al. Sch-net: a deep learning architecture for automatic detection of schizophrenia. Biomed Eng Online. 2021;20(1):75. 36. Huang J, Zhao Y, Qu W, Tian Z, Tan Y, Wang Z, et al. Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network. Asian J Psychiatr. 2022;77:103263. 37. Doborjeh M, Doborjeh Z, Merkin A, Bahrami H, Sumich A, Krishnamurthi R, et al. Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia. Neural Netw. 2021;144:522–39. 38. James C, Ranson JM, Everson R, Llewellyn DJ. Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients. JAMA Netw Open. 2021;4(12):e2136553. 39. You J, Zhang YR, Wang HF, Yang M, Feng JF, Yu JT, et al. Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study. EClinicalMedicine. 2022;53:101665. 40. Salehi AW, Baglat P, Sharma BB, Gupta G, Upadhya A. A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI. En: 2020 International Conference on Smart Electronics and Communication (ICOSEC) [Internet]. 2020 [citado el 8 de enero de 2025]. p. 156–61. Disponible en: https://ieeexplore.ieee.org/document/9215402 41. Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. J Biomed Inform. 2023;137:104254. 42. Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, et al. Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. Lancet Digit Health. junio de 2022;4(6):e426–35. 43. Gomeni R, Bressolle-Gomeni F, Fava M. Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo. Transl Psychiatry. 2023;13(1):141. 44. Joyce JB, Grant CW, Liu D, MahmoudianDehkordi S, Kaddurah-Daouk R, Skime M, et al. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication. Transl Psychiatry. 2021;11(1):513. 45. Taliaz D, Spinrad A, Barzilay R, Barnett-Itzhaki Z, Averbuch D, Teltsh O, et al. Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry. 2021;11(1):381. 46. Sadeh-Sharvit S, Camp TD, Horton SE, Hefner JD, Berry JM, Grossman E, et al. Effects of an Artificial Intelligence Platform for Behavioral Interventions on Depression and Anxiety Symptoms: Randomized Clinical Trial. J Med Internet Res. 2023;25:e46781. 47. Wang M, Hu K, Fan L, Yan H, Li P, Jiang T, et al. Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score. Front Genet. 2022;13:848205. 48. Tandon R. Controversies in psychiatry practice and research. Asian J Psychiatr. 2023;81:103508. 49. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. 50. 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. 51. Castelvecchi D. Can we open the black box of AI? Nature. 2016;538(7623):20–3. 52. Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R. Artificial intelligence and Psychiatry: An overview. Asian J Psychiatr. 2022;70:103021. 53. Yew GCK. Trust in and Ethical Design of Carebots: The Case for Ethics of Care. Int J Soc Robot. 2021;13(4):629–45. 54. Blease C, Locher C, Leon-Carlyle M, Doraiswamy M. Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey. Digit Health. 2020;6:2055207620968355. 55. Tandon R. Computational psychiatry and the psychopathology of psychosis: Promising leads and blind alleys. Schizophr Res. 2023;254:143–5. 56. Riley P. Three pitfalls to avoid in machine learning. Nature. 2019;572(7767):27–9. 57. Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, et al. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med. 2022;5(1):171. 58. Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry. 2020;10(1):116. 59. Sodhani S, Chandar S, Bengio Y. Toward Training Recurrent Neural Networks for Lifelong Learning. Neural Comput. 2020;32(1):1–35. 60. Couckuyt A, Seurinck R, Emmaneel A, Quintelier K, Novak D, Van Gassen S, et al. Challenges in translational machine learning. Hum Genet. 2022;141(9):1451–66. 61. Zhong Y, Lyu YAH, Yu S, Gao YJ, Mi WF, Li JF. The issue of evidence-based medicine and artificial intelligence. Asian J Psychiatr. 2023;85:103627. 62. Hauser TU, Skvortsova V, De Choudhury M, Koutsouleris N. The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health. 2022;4(11):e816–28. 63. Low DM, Rumker L, Talkar T, Torous J, Cecchi G, Ghosh SS. Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. J Med Internet Res. 2020;22(10):e22635. 64. Thornton J, D'Souza R, Tandon R. Artificial intelligence and psychiatry research and practice. Asian J Psychiatr. 2023;81:103509. 65. Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M. From promise to practice: towards the realisation of AI-informed mental health care. Lancet Digit Health. 2022;4(11):e829–40.