Transformación digital en Salud Mental: oportunidades y desafíos en la práctica clínica
Digital transformation in Mental Health: opportunities and challenges in clinical practice
Palabras clave:
Tecnología en salud mental, Salud digital, Adolescencia, Inteligencia artificial y salud mentalResumen
Este artículo examina la creciente integración de la innovación tecnológica en la salud mental, poniendo un énfasis especial en la población adolescente. Se elabora un mapa de diversas herramientas digitales diseñadas para optimizar el diagnóstico, tratamiento y promoción de la salud mental, así como un análisis de las aplicaciones de la inteligencia artificial predictiva y generativa y su impacto en la práctica clínica. Se realiza un análisis crítico de los desafíos clínicos, éticos, operativos y regulatorios que acompañan su implementación, incluyendo aspectos como la validación científica, la protección de la privacidad, la aparición de sesgos en los algoritmos y el riesgo de deshumanización en la asistencia a pacientes. A través de una revisión de la literatura y ejemplos actuales, el artículo ofrece una visión integral destinada a orientar a los profesionales de la salud hacia una adopción responsable y efectiva de soluciones digitales en salud mental.
Citas
World Health Organization. World mental health report: transforming mental health for all. Execu-tive summary. Geneva: World Health Organization; 2022. Disponible en: https://www.who.int/publications/i/item/9789240049338
Solmi M, Radua J, Olivola M, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2021; 27(1):281-295. doi: 10.1038/s41380-021-01161-7.
Kieling C, Buchweitz C, Caye A, et al. Worldwide prevalence and disability from mental disorders across childhood and adolescence: evidence from the Global Burden of Disease Study. JAMA Psychiatry. 2024; 81(4):347-356. doi:10.1001/jamapsychiatry.2023.5051.
Slade T, Johnston A, Oakley Browne MA, et al. 2007 national survey of mental health and wellbe-ing: methods and key findings. Aust N Z J Psychiatry. 2009; 43(7):594-605. doi: 10.1080/00048670902970882.
Children's Commissioner. Lighting review: access to child and adolescent mental health services United Kingdom. 2016. Disponible en: https://www.childrenscommissioner.gov.uk/resource/lightning-review-access-to-child-and-adolescent-mental-health-services/
Islam MI, Yunus FM, Isha SN, et al. The gap between perceived mental health needs and actual service utilization in Australian adolescents. Sci Rep. 2022; 12(1):5430. doi: 10.1038/s41598-022-09352-0.
Radez J, Reardon T, Creswell C, et al. Why do children and adolescents (not) seek and access pro-fessional help for their mental health problems? A systematic review of quantitative and qualitative studies. Eur Child Adolesc Psychiatry. 2021; 30(2):183-211. doi: 10.1007/s00787-019-01469-4.
Torous J, Bucci S, Bell I, et al. The growing field of digital psychiatry: current evidence and the fu-ture of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021; 20(3):318-335. doi: 10.1002/wps.20883.
Torous J, Jän Myrick K, Rauseo-Ricupero N, et al. Digital Mental Health and COVID-19: Using Technology Today to Accelerate the Curve on Access and Quality Tomorrow. JMIR Ment Health. 2020; 7(3):e18848. doi: 10.2196/18848.
Gagnon MP, Sasseville M, Leblanc A. Classification of Digital Mental Health Interventions: A Rapid Review and Framework Proposal. Stud Health Technol Inform. 2022; 294:629-633. doi: 10.3233/SHTI220545.
Pineda BS, Mejia R, Qin Y, et al. Updated taxonomy of digital mental health interventions: a con-ceptual framework. mHealth 2023;9:28. doi: 10.21037.
American Psychological Association. Digital mental health technologies. APA; 2023 [citado febrero de 2025]. Disponible en: https://www.apa.org/practice/digital-therapeutics-mobile-health
Eisenstadt M, Liverpool S, Infanti E, et al. Mobile Apps That Promote Emotion Regulation, Positive Mental Health, and Well-being in the General Population: Systematic Review and Meta-analysis. JMIR Ment Health. 2021; 8(11):e31170. doi: 10.2196/31170.
Cao J, Truong A, Banu S, et al. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Develop-ment and Usability Study. JMIR Mental Health 2020; 7(1):e14045. doi: 10.2196/14045.
American Psychological Association. Proposed Revision of Guidelines for the Practice of Telepsy-chology. APA; 2024. Disponible en: https://www.apa.org/about/policy/telepsychology-revisions
American Psychological Association. Telehealth and telepsychology. APA; 2024 [citado febrero de 2025]. Disponible en: https://www.apa.org/practice/telehealth-telepsychology
Bell I, Nicholas J, Alvarez-Jimenez M, et al. Virtual reality as a clinical tool in mental health re-search and practice. Dialogues Clin Neurosci. 2020; 22 (2):169-77. doi: 10.31887/DCNS.2020.22.2/lvalmaggia.
Boucher EM, Harake NR, Ward HE, et al. Artificially intelligent chatbots in digital mental health in-terventions: a review. Expert Rev Med Devices. 2021; 18(sup1):37-49. doi:10.1080/17434440.2021.2013200.
Digital Therapeutics Alliance. DTA’s Adoption & Interpretation of ISO’s DTx Definition. DTA; 2023 [citado febrero de 2025]. Disponible en: https://dtxalliance.org/wp-content/uploads/2023/06/DTA_FS_New-DTx-Definition.pdf
American Psychological Association. Artificial intelligence in mental health care [Internet]. 2024 [ci-tado 15 de febrero de 2025]. Disponible en: https://www.apa.org/practice/artificial-intelligence-mental-health-care
Ettman C, Galea S. The potential influence of AI on population mental health. JMIR Ment Health. 2023; 10:e49936. doi: 10.2196/49936.
Goh E, Gallo R, Hom J, et al. Large Language Model Influence on Diagnostic Reasoning: A Ran-domized Clinical Trial. JAMA Netw Open. 2024; 7(10):e2440969. doi: 10.1001/jama network open.2024.40969.
Su C, Xu Z, Pathak J, et al. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry. 2020; 10(1):116. doi: 10.1038/s41398-020-0780-3.
Kuhail MA, Alturki N, Thomas J, et al. Human-Human vs Human-AI Therapy: An Empirical Study. International Journal of Human–Computer Interaction. 2024: 1–12. https://doi.org/10.1080/10447318.2024.2385001
D'Hotman D, Loh E. AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health Care Inform. 2020; 27 (3): e100175. doi: 10.1136/bmjhci-2020-100175.
Flemotomos N, Martínez VR, Chen Z, et al. Automated evaluation of psychotherapy skills using speech and language technologies. Behav Res Methods. 2022; 54(2):690-711. doi: 10.3758/s13428-021-01623-4.
Lyssn. Real science driving real change: Academic papers [Internet]. Lyssn; c2025 [citado mayo de 2025]. Disponible en https://www.lyssn.io/the-science/academic-papers/
Zhang TT, Saunders R, Pilling S, et al. CBT Trainer: AI Patient for Training CBT Therapists — a Pi-lot Study [Internet]. 15 abr 2025 [citado 12 mayo 2025]. Disponible en: https://doi.org/10.31234/osf.io/h84qn_v1
Tornero-Costa R, Martínez-Millana A, Azzopardi-Muscat N, et al. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Mental Health 2023; 10: e42045. doi: 10.2196/42045.
APA Services. Steps to evaluate an AI-enabled clinical or administrative tool. APA; 2024 [citado fe-brero de 2025]. Disponible en: https://www.apaservices.org/practice/business/technology/tech-101/evaluating-artificial-intelligence-tool
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Revista del Hospital de Niños de Buenos Aires
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Todo el contenido que se publica en la Revista del Hospital de Niños de Buenos Aires está bajo una Licencia Creative Commons Atribución – No Comercial – Sin Obra Derivada 4.0 Internacional (CC-BY-NC-ND 4.0)
