Abstract
Mental health is a major public health concern around the world, and it should play a prominent role in the healthcare business. However, the rate of progress in this subject appears to be relatively modest. Recently, AI techniques have gained a lot of attention in other industries, including mental health. Current developments highlight AI's revolutionary potential, with applications including early identification of mental health issues, individualized treatment programs, and AI-powered virtual therapists. SoulSense has the potential to lower barriers to mental health care by offering 24-hour access, lowering costs, and reducing the stigma associated with seeking therapy. AI systems may examine enormous information to identify trends that human clinicians may overlook, resulting in more accurate assessments and tailored treatment strategies. However, the implementation of SoulSense in mental health presents considerable hurdles. Concerning areas like data privacy, ethical use, algorithmic prejudice, and AI's inability to completely duplicate human empathy must be addressed. This research investigates how SoulSense can supplement traditional therapy, the advantages and disadvantages of existing AI mental health technologies, and the ethical concerns required for responsible innovation. By evaluating current breakthroughs and case studies, the study hopes to emphasize the potential for AI to complement, rather than replace, human-centered mental health care, ensuring that technology improves rather than degrades therapeutic outcomes.
Metadata
Item Type: | Book Section |
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Creators: | Creators Email / ID Num. Norazhar, Fatin Nabila UNSPECIFIED Jamil, Muhammad Faris Fakhri UNSPECIFIED Baharum, Nuur Aleeysa UNSPECIFIED Roslan, Rohaiza UNSPECIFIED |
Subjects: | A General Works > Academies and learned societies (General) |
Divisions: | Universiti Teknologi MARA, Negeri Sembilan > Seremban Campus |
Page Range: | pp. 314-319 |
Keywords: | Artificial Intelligence, mental health, diagnosis, therapy, ethical concerns |
Date: | 2025 |
URI: | https://ir.uitm.edu.my/id/eprint/119860 |