International Journal on Science and Technology

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AI-Driven Behavioral Insights for Personalized Mental Health.

Author(s) Akanksha Rajendra Bhalsing, Sakshi Rambhau Kul
Country India
Abstract Mental health disorders are a growing global concern, with existing treatments often hindered by limited accessibility, subjectivity in diagnosis, and lack of personalization. This paper explores how Artificial Intelligence (AI) technologies—such as Machine Learning (ML), Natural Language Processing (NLP), and wearable devices—can transform mental health care by enabling early diagnosis, personalized interventions, and continuous monitoring. We highlight recent advancements in digital biomarkers, predictive analytics, and AI-driven teletherapy tools, supported by real-world case studies. Furthermore, the paper addresses key ethical concerns, including data privacy, algorithmic bias, and the importance of transparent and inclusive AI systems. Through a multidisciplinary lens, we advocate for hybrid care models that balance AI innovations with human empathy and outline future directions for research, including explainable AI and multimodal sensitivity analysis. This review underscores the transformative potential of AI in making mental health care more accessible, proactive, and equitable.
Keywords Artificial Intelligence (AI), Mental Health, Personalized Interventions, Machine Learning (ML), Natural Language Processing (NLP), Digital Biomarkers, Predictive Analytics, Ethical AI, Wearable Technology, Teletherapy, Virtual Therapists, Sensitivity Analysis, Explainable AI (XAI), Digital Behavioral Insights, Data Privacy in Healthcare
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-17
Cite This AI-Driven Behavioral Insights for Personalized Mental Health. - Akanksha Rajendra Bhalsing, Sakshi Rambhau Kul - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3686
DOI https://doi.org/10.71097/IJSAT.v16.i2.3686
Short DOI https://doi.org/g9f2g4
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