Palabras clave
Psicoterapia
Inteligencia artificial
Informática y desarrollo
Salud mental
Inteligencia artificial
Informática y desarrollo
Salud mental
Cómo citar
Sotelo-Martín, J.-A. (2025). Psicoterapia e inteligencia artificial: Diálogos clínicos potenciados por algoritmos artificiales para humanizar el cuidado emocional. Revista Latinoamericana De Difusión Científica, 7(13), 138-161. https://doi.org/10.5281/zenodo.15843790
Resumen
Este trabajo analiza el impacto actual y proyectado de la inteligencia artificial (IA) en psicoterapia, psiquiatría y psicología clínica. Su OBJETIVO es valorar cómo estas tecnologías pueden optimizar el diagnóstico, la intervención terapéutica y el acceso al cuidado emocional sin reemplazar el vínculo humano. Mediante una METODOLOGÍA de revisión narrativa, se sintetizaron aportes teóricos y empíricos recientes que muestran que la IA, lejos de deshumanizar la práctica clínica, puede reforzarla cuando se aplica éticamente. Los RESULTADOS destacan siete áreas clave de aplicación: detección precoz de síntomas mediante algoritmos entrenados; acompañamiento emocional a través de chatbots empáticos; personalización de intervenciones basadas en biomarcadores, lenguaje y narrativas; uso de realidad virtual para exposiciones controladas; apoyo a poblaciones rurales; generación automatizada de informes clínicos y mejora en la adherencia terapéutica. También se subrayan desafíos éticos como el sesgo algorítmico, la privacidad de los datos y la supervisión profesional. En CONCLUSIÓN, la IA no sustituye al terapeuta, sino que amplifica su capacidad diagnóstica y relacional. Su integración responsable fortalece una atención más precisa, equitativa y humanizada, capaz de responder a las demandas contemporáneas de la salud mental.
Citas
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Haque, M. D. R. & Rubya, S. (2023). An overview of chatbot-based mobile mental health apps: Insights from app description and user reviews. JMIR Mental Health and uHealth, 11, e44838. https://doi.org/10.2196/44838
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Jinbo, H. & Feng, J. (2025). Artificial Intelligence and Social Media for the Detection of Eating Disorders. 02-76-3478. International Journal of eating disorders, https://doi.org/10.1002/eat.24438
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Kadirvelu, B., Bellido Bel, T., Freccero, A., Di Simplicio, M., Nicholls, D. & Faisal, A. A. (2025). Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data. arXiv preprint arXiv:2501.08851. https://arxiv.org/abs/2501.08851
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Babu, A. & Akhil, P. J. (2024). Artificial intelligence in mental healthcare: transformative potential vs ethical challenges. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1378904
Beele, G., Liesong, P., Bojanowski, S., Hildebrand, K., Weingart, M., Asbrand, J., Correll, C. U., Morina, N., Uhlhaas, P. J. (2024). Virtual-reality exposure therapy for reducing school anxiety in adolescents: Pilot study. JMIR Mental Health, 11, e56235. https://doi.org/10.2196/56235
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Döllinger, N., Wolf, E., Mal, D., Wenninger, S., Botsch, M., Latoschik, M. E. & Wienrich, C. (2022). Resize Me! Exploring the User Experience of Embodied Realistic Modulatable Avatars for Body Image Intervention in Virtual Reality. arXiv preprint arXiv:2203.05060. https://arxiv.org/abs/2203.05060
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Held, P., Pridgen, S. A., Chen, Y., Akhtar, Z., Amin, D. & Pohorence, S. (2024). A Novel Cognitive Behavioral Therapy–Based Generative AI Tool (Socrates 2.0) to Facilitate Socratic Dialogue: Protocol for a Mixed Methods Feasibility Study. JMIR Research Protocolé, 13. https://doi.org/10.2196/58195
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Jun, J., Dong, W., Jiaqi, L., Jingzhu, P., Chaonan, F., Rujia, L., Chuan, S. & Yantao, M. (2024). Depressive and mania mood state detection through voice as a biomarker. Frontiers in Neurology, 15, 1394210. https://doi.org/10.3389/fneur.2024.1394210
Kadirvelu, B., Bellido Bel, T., Freccero, A., Di Simplicio, M., Nicholls, D. & Faisal, A. A. (2025). Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data. arXiv preprint arXiv:2501.08851. https://arxiv.org/abs/2501.08851
Karantonis, J. A., Susan L. Rossell,S. L., Berk, M. & Van Rheenen, T. E. (2021). The effect of virtual reality based cognitive behavioral therapy in patients with depressive disorder: A meta-analysis of randomized controlled trials. Journal of Affective Disorders, 282, 776-783. https://doi.org/10.1016/j.jad.2020.12.186
Lan, L., Ke, L. & Diao, L. (2024). Exploring the application of AI in the education of children with autism: A Public Health-Driven Transformer model. Frontiers in Psychiatry, 15, 1521926. https://doi.org/10.3389/fpsyt.2024.1521926
Lee, Y. K., Lee, I., Shin, M., Bae, S. & Hahn, S. (2024). Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models. Korean Journal of Cognitive Science, 35(1), 23-48. https://doi.org/10.48550/arXiv.2311.04915
Linardon, J., Fitzsimmons-Craft, E. E. & Wilksch, S. (2025). AI-Readiness in the Eating Disorders Field. International Journal of Eating Disorders, 58(4), 724-734. https://doi.org/10.1002/eat.24385
Li, X., Kang, Q. & Gu, H. (2023). A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Frontiers in Human Neuroscience, 17, 1280512. https://doi.org/10.3389/fnhum.2023.1280512
Lønfeldt, N. N., Clemmensen, L. K. H. & Pagsberg, A. K. (2023). A wearable artificial intelligence feedback tool (wrist angel) for treatment and research of obsessive-compulsive disorder: protocol for a nonrandomized pilot study. JMIR Research Protocols, 12, e45123. https://doi.org/10.2196/45123
MacNeill, A. L., Doucet, S. & Luke, A. (2024). Effectiveness of a mental health chatbot for people with chronic conditions: Randomized controlled trial. JMIR Formative Research, 8, e50025. https://doi.org/10.2196/50025
Mahmood, M. A., Jamel, L., Alturki, N. & Tawfeek, A. M. (2025). Leveraging artificial intelligence for diagnosis of children autism through facial expressions. Scientific Reports. 15, 11945. https://doi.org/10.1038/s41598-025-96014-6
Monaco, F, Vignapiano, A., Piacente, M., Pagano, C., Mancuso, C., Steardo, L., Marenna, A., Farina, F., Petrillo, G., Leo, S., Ferrara, E., Palermo, S., Martiadis, V., Solmi, M., Monteleone A. M., Fasano, A. & Corrivetti, G. (2024). An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders. Frontiers in Psychiatry, 15, 1414439. https://doi.org/10.3389/fpsyt.2024.1414439
Mullick, T., Radovic, A., Shaaban, S. & Doryab, A. (2022). Predicting depression in adolescents using mobile and wearable sensors: Multimodal machine-learning exploratory study. JMIR Formative Research, 6(6), e35807. https://doi.org/10.2196/35807
Naseriparsa, M., Sukunesan, S., Cai, Z., Alfarraj, O., Tolba, A., Rabooki, S. F. & Xia, F. (2025). ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification. arXiv preprint arXiv:2501.14785. https://arxiv.org/abs/2501.14785
Ohse, J., Hadžić, B., Mohammed, P., Peperkorn, N., Fox, J., Krutzki, J., Lyko, A., Mingyu, F., Zheng, X., Rätsch, M. & Shiban, Y. (2024). GPT-4 shows potential for identifying social anxiety from clinical interview data. Scientific Reports, 14, 30498. https://doi.org/10.1038/s41598-024-82192-2
Omar, M., Soffer, S., Charney, A. W., Landi, I., Nadkarni, G. N. & Klang, E. (2024). Applications of large language models in psychiatry: A systematic review. Frontiers in Psychiatry, 15, 1422807. https://doi.org/10.3389/fpsyt.2024.1422807
Pan, Y., Wang, P, Xue, B., Yanbin, Y., Shen, S., Wang, S. & Xing W. (2025). Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1515549
Patel, K., Tripathy, A. K., Padhy, L. N., Kar, S. K., Padhy, S. K., & Mohanty, S. P. (2022). Accu-Help: A machine learning based smart healthcare framework for accurate detection of obsessive-compulsive disorder. arXiv preprint arXiv:2212.02346. https://arxiv.org/abs/2212.02346
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