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Real-Time AI-Enhanced Chatbots for Mental Health Support
Subject area: Science,Engineering and Technology · Area of research: AI in Healthcare
Abstract
Mental health disorders represent one of the most pressing global health challenges of our time, contributing significantly to the worldwide disease burden and economic losses. Traditional therapeutic approaches struggle to meet the escalating demand for mental health services, leaving millions without adequate care. This research paper explores the transformative potential of AI-enhanced chatbots in revolutionizing mental health support systems. By leveraging Natural Language Processing (NLP), sentiment analysis, and emotion recognition technologies, these intelligent systems provide scalable, accessible, and stigma-free support available 24/7. This paper examines the architecture, technologies, key features, benefits, and ethical considerations of real-time AI chatbots designed for mental health applications. The findings demonstrate that while these systems cannot replace human therapists, they serve as crucial bridges to professional care, significantly improving accessibility and early intervention capabilities.
Keywords
Artificial Intelligence, Chatbots, Mental Health, NLP, Sentiment Analysis, Healthcare Technology, Digital Therapeutics
References
[1] World Health Organization. (2024). Global Mental Health Status Report. WHO Publications.
[2] International Labour Organization. (2024). The Economic Impact of Mental Health Conditions on Global Productivity. ILO Technical Report.
[3] Smith, A., & Johnson, B. (2023). AI Applications in Mental Health: A Systematic Review. Journal of Digital Health,12(4), 234-256.
[4] Chen, L., et al. (2023). Chatbot-Assisted Therapy: Efficacy in Depression and Anxiety Treatment. Mental Health Technology Review, 8(2), 145-168.
[5] Patel, R., & Kumar, S. (2023). Ethical Frameworks for AI in Healthcare Applications. Healthcare Ethics Quarterly,15(3), 289-305.
[6] Anderson, M., Thompson, J., & Williams, K. (2023). Privacy and Security in Digital Mental Health Systems. Cybersecurity in Healthcare, 7(1), 56-78.
[7] Euroean Commission. (2023). Regulatory Guidelines for AI-Based Healthcare Interventions. EU Digital Health Initiative.
[8] National Institute of Mental Health. (2024). Emerging Technologies in Mental Health Care: Implementation Guidelines. NIMH Research Reports.
[9] Kim, H., Lee, J., & Park, S. (2023). Natural Language Processing for Emotional Intelligence in Chatbot Systems. AIand Psychology Review, 11(4), 312-334.
[10] Thompson, E., & Martinez, R. (2023). Long-Term Outcomes of Chatbot-Assisted Mental Health Interventions: A Five -Year Follow-Up Study. Journal of Clinical Psychology and Technology, 14(2), 167-189.
How to cite this paper
@article{1712354,
author = {Khushi Rajput, Komal, Nilesh Kumar Pandey, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri},
title = {Real-Time AI-Enhanced Chatbots for Mental Health Support},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {2086-2093},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712354.pdf},
abstract = {Mental health disorders represent one of the most pressing global health challenges of our time, contributing significantly to the worldwide disease burden and economic losses. Traditional therapeutic approaches struggle to meet the escalating demand for mental health services, leaving millions without adequate care. This research paper explores the transformative potential of AI-enhanced chatbots in revolutionizing mental health support systems. By leveraging Natural Language Processing (NLP), sentiment analysis, and emotion recognition technologies, these intelligent systems provide scalable, accessible, and stigma-free support available 24/7. This paper examines the architecture, technologies, key features, benefits, and ethical considerations of real-time AI chatbots designed for mental health applications. The findings demonstrate that while these systems cannot replace human therapists, they serve as crucial bridges to professional care, significantly improving accessibility and early intervention capabilities.},
keywords = {Artificial Intelligence, Chatbots, Mental Health, NLP, Sentiment Analysis, Healthcare Technology, Digital Therapeutics},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712354}
}