Analysis of AI Techniques for Depression Diagnosis

This paper will provide a comprehensive analysis of the latest developments and the ongoing difficulties in applying machine learning to improve facial expression analysis-based early depression detection. These techniques have several benefits, including non-invasiveness, scalability to larger populations, and the ability to detect subtle emotional indicators that are frequently linked to depressive moods. For this reason, the paper considers important issues that are by nature of this area, such as reducing bias from datasets, interpreting results produced by machine learning in the clinical setting, and bargaining through various ethical issues concerning data protection and informed permission. It is an attempt at the review of these changes via an amalgamation of previous studies' outcomes and assessing technological advancement. This it does by emphasizing new research directions and creative techniques in the field that warrant further research.