AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
Source abstract
Abstract
Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential benefits in improving mental and emotional well-being, addressing specific mental health conditions, and facilitating behavior change. However, challenges related to usability, engagement, and integration with existing healthcare systems were identified. AI chatbots hold promise for mental health interventions, but widespread adoption hinges on improving usability, engagement, and integration with healthcare systems. Enhancing personalization and context-specific adaptation is key. Future research should focus on large-scale trials, optimal human–AI integration, and addressing ethical and social implications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Mental Health Interventions · Mental Health Research Topics · Mental Health via Writing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
Department of Educational Sciences, Section of Psychology, University of Catania, 95124 Catania, Italy
Center of Excellence for the Acceleration of Harm Reduction (CoEHAR), University of Catania, 95123 Catania, Italy
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