The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review
This is an incremental review article summarizing existing research on AI applications in psychiatry detection, aimed at researchers and practitioners in mental health and AI.
The paper reviews the use of machine learning and interpretable AI methods for detecting depression, highlighting their importance in improving diagnosis accuracy and speed during the COVID-19 pandemic, but does not present new experimental results or concrete numbers.
The COVID-19 pandemic has forced many people to limit their social activities, which has resulted in a rise in mental illnesses, particularly depression. To diagnose these illnesses with accuracy and speed, and prevent severe outcomes such as suicide, the use of machine learning has become increasingly important. Additionally, to provide precise and understandable diagnoses for better treatment, AI scientists and researchers must develop interpretable AI-based solutions. This article provides an overview of relevant articles in the field of machine learning and interpretable AI, which helps to understand the advantages and disadvantages of using AI in psychiatry disorder detection applications.