A Systematic Review on Artificial Intelligence-Based Clinical Decision Support Systems in Depression.
This systematic review examines the current landscape of artificial intelligence (AI)-based Clinical Decision Support Systems (CDSS) designed to aid in the treatment of depression. With depression recognized as a complex and multifactorial condition, often differing across contexts such as postpartum, adolescence, and elderly populations, the review aims to evaluate the feasibility, acceptability, and potential of AI-based CDSS in supporting personalized care. The primary research question explores how these systems are implemented and evaluated in clinical settings, as well as the main challenges to their broader adoption.
The review analyzed studies focused on AI-based CDSS for depression, identifying variations in algorithms, performance metrics, and implementations. Studies were assessed based on system purposes, categorized into treatment selection, prediction and risk assessment, and clinical support and data review, providing a comprehensive overview of each study's approach and outcomes.
11 unique algorithms, 9 performance metrics, and 10 CDSS implementations were identified across the studies. Findings indicate positive results for CDSS in supporting clinical decision-making, yet highlight challenges in applying these tools in routine practice. Limitations include small, non-diverse sample sizes, immature methodologies, and difficulties in workflow integration, along with concerns about data privacy and the ethical use of AI.
AI-based CDSS for depression shows potential in patient care; however, foundational issues such as more diverse samples and robust model development remain critical. Addressing these aspects will set the stage for future research into advanced applications, such as virtual-reality integration and multi-condition prediction, thus advancing AI's role in mental health treatment.Clinical Relevance-This work illustrates the state of art of AI adoption in the domain of mental health, specifically in the context of depression.
The review analyzed studies focused on AI-based CDSS for depression, identifying variations in algorithms, performance metrics, and implementations. Studies were assessed based on system purposes, categorized into treatment selection, prediction and risk assessment, and clinical support and data review, providing a comprehensive overview of each study's approach and outcomes.
11 unique algorithms, 9 performance metrics, and 10 CDSS implementations were identified across the studies. Findings indicate positive results for CDSS in supporting clinical decision-making, yet highlight challenges in applying these tools in routine practice. Limitations include small, non-diverse sample sizes, immature methodologies, and difficulties in workflow integration, along with concerns about data privacy and the ethical use of AI.
AI-based CDSS for depression shows potential in patient care; however, foundational issues such as more diverse samples and robust model development remain critical. Addressing these aspects will set the stage for future research into advanced applications, such as virtual-reality integration and multi-condition prediction, thus advancing AI's role in mental health treatment.Clinical Relevance-This work illustrates the state of art of AI adoption in the domain of mental health, specifically in the context of depression.
Authors
Chiang Chiang, Coll Coll, Pollo-Cattaneo Pollo-Cattaneo, Chatterjee Chatterjee
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