The field of mental health diagnosis is rapidly evolving with the integration of artificial intelligence (AI) and machine learning (ML) techniques. Recent studies have focused on developing innovative approaches to diagnose mental health conditions such as depression and anxiety. One notable direction is the use of natural language processing (NLP) and large language models (LLMs) to analyze clinical notes, social media posts, and other forms of text data. These models have shown promising results in detecting depressive symptoms and generating human-interpretable reasoning. Another area of research is the development of simulated patients and virtual simulation environments to support diagnostic model training and evaluation. The use of multimodal datasets, including audio, video, and functional near-infrared spectroscopy (fNIRS) signals, is also becoming increasingly popular. Furthermore, researchers are exploring the application of root cause analysis (RCA) training for healthcare professionals using AI-powered virtual simulation. Noteworthy papers in this area include: The paper on ReDSM5, which introduces a novel Reddit corpus annotated with DSM-5 depression symptoms and provides baseline benchmarks for multi-label symptom classification and explanation generation. The paper on TalkDep, which proposes a clinician-in-the-loop patient simulation pipeline to develop simulated patients with diversified profiles for diagnostic model training and evaluation. The paper on VS-LLM, which presents a visual-semantic depression assessment method based on LLM for drawing projection tests and demonstrates improved performance compared to traditional psychologist assessment methods.