“Sistemas de Apoyo a la Toma de Decisiones Clínicas basados en Inteligencia Artificial para el Manejo de la Depresión”.
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Date
2025-06-26
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Escuela de Posgrado FRBA
Abstract
La depresión representa un problema prioritario de salud pública por su elevada prevalencia, su impacto debilitante y su crecimiento sostenido a nivel global. Ante dicha realidad, la Inteligencia Artificial (IA) emerge como una herramienta prometedora en el ámbito clínico, particularmente a través de su integración en los sistemas de apoyo a la toma de decisiones clínicas (CDSS, por sus siglas en inglés).
El objetivo del presente final es analizar el estado actual del uso de CDSS basados en IA aplicados al abordaje de la depresión, destacando sus beneficios, limitaciones y desafíos, con el objetivo de aportar conocimiento riguroso que oriente futuros desarrollos tecnológicos y clínicos. Para ello, se realiza una revisión sistemática de estudios primarios publicados entre 2015 y 2024, disponibles en inglés, a través de las bases de datos IEEE Xplore, ScienceDirect, PubMed y SpringerLink.
Como resultado, se seleccionan quince estudios que cumplen con los criterios de inclusión, los cuales abordan diferentes enfoques en la aplicación de IA en CDSS, tales como la selección de tratamientos, la predicción de riesgos y el apoyo en la evaluación clínica. En general, los resultados muestran un alto potencial de dichas herramientas para complementar la toma de decisiones médicas, aunque también revelan limitaciones comunes, como el uso de muestras pequeñas, la falta de diversidad en los datos y la escasa validación en entornos clínicos reales.
Pese a tales desafíos, la evidencia encontrada indica que los CDSS con IA podrían mejorar la precisión, eficiencia y personalización del tratamiento de la depresión. Además, se destaca la necesidad de seguir investigando, con estudios más amplios, diversos y orientados a la práctica clínica, que permitan fortalecer su adopción futura.
Depression represents a major public health concern due to its high prevalence, debilitating impact, and sustained global growth. In light of this reality, Artificial Intelligence (AI) emerges as a promising tool in the clinical field, particularly through its integration into Clinical Decision Support Systems (CDSS). The objective of this study is to analyze the current state of AI-based CDSS applied to the management of depression, highlighting their benefits, limitations, and challenges, with the aim of contributing rigorous knowledge to guide future technological and clinical developments. To this end, a systematic review is conducted on primary studies published between 2015 and 2024, available in English, using the IEEE Xplore, ScienceDirect, PubMed, and SpringerLink databases. As a result, fifteen studies are selected that meet the inclusion criteria. These studies present different approaches to the application of AI in CDSS, including treatment selection, risk prediction, and support for clinical evaluation. Overall, the findings show strong potential for these tools to complement medical decision-making, while also revealing common limitations such as small sample sizes, lack of data diversity, and limited validation in real-world clinical settings. Despite these challenges, the evidence suggests that AI-enabled CDSS could enhance the accuracy, efficiency, and personalization of depression treatment. Moreover, the need for continued research is emphasized, with broader, more diverse, and practice-oriented studies required to strengthen future adoption.
Depression represents a major public health concern due to its high prevalence, debilitating impact, and sustained global growth. In light of this reality, Artificial Intelligence (AI) emerges as a promising tool in the clinical field, particularly through its integration into Clinical Decision Support Systems (CDSS). The objective of this study is to analyze the current state of AI-based CDSS applied to the management of depression, highlighting their benefits, limitations, and challenges, with the aim of contributing rigorous knowledge to guide future technological and clinical developments. To this end, a systematic review is conducted on primary studies published between 2015 and 2024, available in English, using the IEEE Xplore, ScienceDirect, PubMed, and SpringerLink databases. As a result, fifteen studies are selected that meet the inclusion criteria. These studies present different approaches to the application of AI in CDSS, including treatment selection, risk prediction, and support for clinical evaluation. Overall, the findings show strong potential for these tools to complement medical decision-making, while also revealing common limitations such as small sample sizes, lack of data diversity, and limited validation in real-world clinical settings. Despite these challenges, the evidence suggests that AI-enabled CDSS could enhance the accuracy, efficiency, and personalization of depression treatment. Moreover, the need for continued research is emphasized, with broader, more diverse, and practice-oriented studies required to strengthen future adoption.
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Keywords
Inteligencia Artificial, sistema de apoyo a la toma de decisiones clínicas, depresión