AI-Driven Tools for Detecting and Monitoring Mental Health Conditions Though Behaviour Patterns
Kalpana Kasaudhan
Kalpana Kasaudhan, Department of Computer Application, Babu Banarasi Das University, Lucknow (Uttar Pradesh), India.
Manuscript received on 27 December 2024 | First Revised Manuscript received on 03 January 2025 | Second Revised Manuscript received on 18 February 2025 | Manuscript Accepted on 15 March 2025 | Manuscript published on 30 March 2025 | PP: 14-19 | Volume-5 Issue-3, March 2025 | Retrieval Number: 100.1/ijpmh.B105405020125 | DOI: 10.54105/ijpmh.B1054.05030325
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artificial intelligence (AI) is revolutionizing the field of mental health treatment through the application of machine learning, natural language processing, digital phenotyping, and chatbot technologies, offering unprecedented opportunities for widespread mental health support, continuous monitoring, personalized therapy, and early diagnosis. Emerging innovations such as AI-generated synthetic data, augmented reality (AR), and brain-computer interfaces (BCIs) present promising solutions to address persistent challenges in mental health care, including accessibility and personalization. These advancements hold the potential to significantly enhance treatment outcomes, broaden the reach of mental health services, and provide more proactive interventions. However, as AI technologies advance, it is critical to address ethical, privacy, and legal concerns to ensure responsible development and deployment. By carefully navigating these issues, AI can democratize mental health care, making it more accessible, effective, and efficient on a global scale. The integration of AI into mental health services, when managed properly, could lead to a more inclusive and equitable approach to mental health treatment, reshaping the landscape of mental health care for future generations.
Keywords: Artificial Intelligence, Mental Health Treatment, Machine Learning, Natural Language Processing, Digital Phenotyping, Chatbot Technologies, Augmented Reality, Brain Computer Interfaces, Ethical Concerns, Privacy, Accessibility
Scope of the Article: Maternal Health