Paper Key : IRJ************642
Author: Harsh Pathak
Date Published: 01 Jul 2024
Abstract
This paper explores the intersection of artificial intelligence (AI) and mental health, addressing the global surge in mental health issues. The introduction emphasizes pivotal role in transforming mental health care, offering innovative solutions for prevention and treatment. The overview delves into understanding mental health, machine learning concepts, and the significance of natural language processing (NLP) in mental health analysis. Challenges and ethical considerations in AI-driven mental health care are discussed, emphasizing the need for a balance between technological advancements and ethical considerations. The study concludes by highlighting the integration of AI into mental health services, transforming diagnosis, and treatment while emphasizing the importance of addressing ethical and privacy concerns. The need for the study is established by recognizing the growing mental health crisis and the limitations of traditional assessment methods. AI algorithms, capable of analyzing diverse data sources, are positioned as a solution for early detection, intervention, and personalized treatment plans. The objectives of the study include exploring current methodologies for mental health assessment, conducting a comparative analysis of various technologies, and proposing an AI model for early detection. The research methodology involves a systematic approach, including a literature review, comparative analysis, and gathering data for sentiment analysis to predict accurate mental health status. The problem statement focuses on the critical challenge of timely detection of mental health conditions, advocating for robust AI-driven systems for early detection. Challenges such as limited labeled datasets, algorithmic fairness, privacy concerns, and integration into clinical workflows are identified. The literature review provides an overview of existing research, highlighting AI applications in mental health, such as online therapy platforms, deep learning models, and natural language processing. The comparative analysis further evaluates the strengths and limitations of various studies, emphasizing the need for multidisciplinary approaches and further exploration in deep learning. The conclusion summarizes key findings, emphasizing the transformative potential of AI in mental health care and the importance of addressing ethical considerations. The future scope envisions advancements in early detection through refined technologies, integration of multimodal data sources, and user-friendly interfaces for active participation in mental health assessment and treatment. The dissertation sets the stage for shaping the future trajectory of AI in mental health, guiding further research and development in this transformative field.
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