Deep And Machine Learning in Psychology- A Survey of Depression Detection, Diagnosis, and Treatment Progress
Keywords:
Psychiatry, Artificial intelligence, Depression, Deep learning, Neural networks, Treatment response predictionAbstract
Recent research focuses on mental health and brain informatics. Emerging technologies like AI, deep learning, and machine learning drove the advancements. Customizing, diagnosing, and treating depression with data-driven approaches could improve mental health care. A growing field, precision psychiatry uses cutting-edge computer tools to provide tailored mental health care. AI in precision psychiatry is examined in this paper. Complex formulations aid therapy. These tools can identify and treat mental health patients. They can customize therapies for most patients. Unsupervised learning algorithms have shown considerable sadness-related sickness disparities. These methods separate diagnostic categories. Artificial intelligence could help us suggest drugs based on facts, not group averages. Our findings show that data-driven paradigms in healthcare face several challenges. Surprisingly, none of the survey studies reveal how current procedures improve patient outcomes. Standardizing field terminology, forming diverse research teams, evaluating models, identifying flaws, and making datasets accessible are crucial. Randomized controlled trials must show that computer algorithms improve patient outcomes to make models more feasible.
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