3066-280XISSN (Online)
Volume 4 , Issue 2 , PP: 01-12, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Asifa Iqbal 1 *
Doi: https://doi.org/10.54216/MOR.040201
The incredible progress seen in artificial intelligence and the generative deep learning component has catalyzed improvements in diagnosing and treating mental illnesses, something promising for the mental health field today. The review takes a deep dive into various generative deep learning strategies (for instance, GANs, VAEs, and transformers) and their application in mental health. These technologies can also offer better action to analyze the data even before the disorder is fully blown, looking at the patterns of the data collected on individual patients. In addition, we assess the ethical concerns and barriers to adopting such sophisticated methods in healthcare practice, including data management, fairness, and the monitoring of these techniques by professionals. It is argued that generative deep learning can disrupt mental healthcare in a positive way as new ideas that do not even exist in therapies today can be proposed and used to supplement available therapies, which will enhance the quality of care that patients receive and will improve the outcomes. Furthermore, we explore new approaches to research focused on the use of generative models in mental health, calling attention to the need for cross-disciplinary cooperation that would allow us to make the most of these technologies for the benefit of clinical practice and offer them to different groups of patients.
Generative deep learning , Mental health diagnostics , Personalized therapy , GANs, Data privacy , Early intervention
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