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Artificial Intelligence Technologies in Mental Health: Transforming Depression Care Through Innovation
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Author(s): Elvira Nurfadhilah (BRIN, Indonesia), Ambar Yoganingrum (BRIN, Indonesia), Andi Djalal Latief (BRIN, Indonesia), Armita Widyasuri (BRIN, Indonesia), Asril Jarin (BRIN, Indonesia), Dian Isnaeni Nurul Afra (BRIN, Indonesia), Gunarso Gunarso (BRIN, Indonesia), Kokoy Siti Komariah (BRIN, Indonesia), Mohammad Teduh Uliniansyah (BRIN, Indonesia), Nimas Ayu Untariyati (BRIN, Indonesia), Nuraisa Novia Hidayati (BRIN, Indonesia), Radhiyatul Fajri (BRIN, Indonesia), Retno Anggreini Dyah Ayuningtias (BRIN, Indonesia), Siska Pebiana (BRIN, Indonesia), Yaniasih Yaniasih (BRIN, Indonesia), Yuyun Yuyun (BRIN, Indonesia), Hayuning Titi Karsanti (BRIN, Indonesia)and Gita Citra Puspita (BRIN, Indonesia)
Copyright: 2025
Pages: 44
Source title:
Humanizing Technology With Emotional Intelligence
Source Author(s)/Editor(s): Subrata Tikadar (Amity University, Kolkata, India), Haipeng Liu (Coventry University, UK), Pronaya Bhattacharya (Amity University Kolkata, India)and Samit Bhattacharya (Indian Institute of Technology Guwahati, India)
DOI: 10.4018/979-8-3693-7011-7.ch011
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Abstract
This chapter explores the incorporation of artificial intelligence (AI) into mental health care, with a particular focus on managing depression. AI has significantly enhanced the promotion, detection, diagnosis, treatment, and monitoring of depression by leveraging technologies such as machine learning, natural language processing, and wearable devices. This chapter also discusses various AI-driven approaches, including the analysis of questionnaires, medical records, social media, speech data, electroencephalogram, magnetic resonance imaging, chatbots, virtual reality, face analysis, robots, multimodal methods, and wearable devices. Each of these technologies offers unique benefits, such as increased accuracy in detecting depression, personalized treatment plans, and continuous patient monitoring. However, the challenges linked to AI in mental health, such as data privacy issues, biases in algorithms, and the complexity of human emotions. The chapter concludes by highlighting the opportunities and future research directions and innovation for AI in enhancing of depression care.
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