3066-280XISSN (Online)
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.
Read MoreDoi: https://doi.org/10.54216/MOR.040201
Vol. 4 Issue. 2 PP. 01-12, (2025)
Energy consumption worldwide is increasing due to increased populations, industrialization, and technological development, underlining the importance of efficient energy use. Waste-to-energy technologies are also known as waste-to-energy systems, whereby the production of Energy and Waste Management are considered interrelated. This review summarizes the present trends and state–of–the–art waste management technologies, where renewable energy systems have been integrated into waste management infrastructure and how optimization algorithms help to improve waste management systems. Anaerobic digestion, pyrolysis, and gasification processes raise wastes and convert them into energy products like biogas and syngas, which follow material flow and recovery. Another important area covered in the study is implementing machine learning-optimized methods, genetic algorithms, and artificial neural networks for waste processing and energy recovery. These threats become as follows: high capital costs, feedstock fluctuations, and public perception are tackled alongside solutions like policy support or engagement of the communities involved. This review focuses on the importance of multi-disciplinary systems to achieve future sustainable Waste-to-Energy systems for both the global environment and energy objectives.
Read MoreDoi: https://doi.org/10.54216/MOR.040202
Vol. 4 Issue. 2 PP. 13-23, (2025)
We should subject artificial intelligence (AI) to neurological diagnostics for detailed ethical consideration and examination of compliance questions. When applied to neuroimaging, these AI technologies improve diagnostic performance and treatment planning; however, they give rise to issues such as algorithmic bias, data privacy, and the intelligibility of resulting AI-generated insights. The issue of bias is related to the necessity of obtaining informed consent because of using patient data for training models of AI, which in turn will create more problems since the machine learning process will be based on data that is itself bigoted. In addition, the self-governing characteristic of AI systems creates additional concerns regarding responsibility for misuse; it is still unclear who is to blame when an AI system commits an obvious mistake, like misdiagnosis or incorrect treatment. Governance structures must adapt to these questions to guarantee that healthcare AI is ethically upraised, transparent, and fair. This review underscores the importance of interprofessional relationships between researchers and scholars, clinicians and practitioners, and ethicists when dealing with these issues. As social safeguards, demographic benchmarks and best practices have to be set, it enables the medical field to benefit from the opportunities provided by AI in neurological diagnostics and uphold the patient's respect for their rights while pushing for equal access to equal quality health care. Lastly, it becomes imperative to counter these ethical questions, which is imperative for the effectiveness of AI technologies and for building public acceptance of this technology in clinical practice.
Read MoreDoi: https://doi.org/10.54216/MOR.040203
Vol. 4 Issue. 2 PP. 24-32, (2025)
We should subject artificial intelligence (AI) to neurological diagnostics for detailed ethical consideration and examination of compliance questions. When applied to neuroimaging, these AI technologies improve diagnostic performance and treatment planning; however, they give rise to issues such as algorithmic bias, data privacy, and the intelligibility of resulting AI-generated insights. The issue of bias is related to the necessity of obtaining informed consent because of using patient data for training models of AI, which in turn will create more problems since the machine learning process will be based on data that is itself bigoted. In addition, the self-governing characteristic of AI systems creates additional concerns regarding responsibility for misuse; it is still unclear who is to blame when an AI system commits an obvious mistake, like misdiagnosis or incorrect treatment. Governance structures must adapt to these questions to guarantee that healthcare AI is ethically upraised, transparent, and fair. This review underscores the importance of interprofessional relationships between researchers and scholars, clinicians and practitioners, and ethicists when dealing with these issues. As social safeguards, demographic benchmarks and best practices have to be set, it enables the medical field to benefit from the opportunities provided by AI in neurological diagnostics and uphold the patient's respect for their rights while pushing for equal access to equal quality health care. Lastly, it becomes imperative to counter these ethical questions, which is imperative for the effectiveness of AI technologies and for building public acceptance of this technology in clinical practice.
Read MoreDoi: https://doi.org/10.54216/MOR.040204
Vol. 4 Issue. 2 PP. 33-41, (2025)
In this case, the diagnostic and statistical manual for mental disorders has experienced increased advancements in deep generative models (DGMs) that incorporate deep learning in analyzing neuroimaging information. The following review looks at different approaches that have been used in the classification of mental disorders and the specific performance of DGMs like GANs and VAEs. In classifying psychiatric symptoms, it remains challenging to represent the inherent intricacy of data by conventional methods. Thus, techniques that are more accurate are needed to identify complex patterns in extensive data. The newer studies also suggest that DGMs yield higher accuracy than traditional machine learning approaches because the most important features can be identified without requiring significant feature engineering. For example, using GANs to distinguish between major depressive disorder and healthy controls surpasses traditional classifier accuracy by remarkable margins. Moreover, this review contrasts the DGM architectures and their implementations in various psychiatric disorders that can improve diagnostic accuracy and pathophysiological features of diseases. Altogether, the results of the present study emphasize the possibilities of DGMs’ contribution to the field of psychiatry and open possibilities for further studies to deliver more precise diagnostic classifications and enhance the efficacy of treatment by employing the perspective of personalized medicine.
Read MoreDoi: https://doi.org/10.54216/MOR.040205
Vol. 4 Issue. 2 PP. 42-52, (2025)