Depressive and anxiety disorders are widespread, necessitating timely identification and management. These conditions manifest through various emotional and behavioral symptoms, such as persistent sadness and excessive worry. When left undiagnosed and untreated, these disorders can cause severe consequences, including increased risk of suicide and substantial socioeconomic burden. Recent advances in Large Language Models (LLMs) offer potential solutions, yet high costs and ethical concerns about training data remain challenges. This paper introduces a pipeline for synthesizing clinical interviews, resulting in 1,157 interactive dialogues (PsyInterview), and presents EmoScan, an LLM-based emotional disorder screening system. EmoScan distinguishes between coarse (e.g., anxiety or depressive disorders) and fine disorders (e.g., major depressive disorders) and conducts high-quality interviews. Evaluations show that EmoScan exceeds the performance of base models and other LLMs like GPT-4 in screening emotional disorders (F1-score = 0.7467). It also delivers superior explanations (BERTScore=0.9408) and demonstrates robust generalizability (F1-score of 0.67 on an external dataset). Furthermore, EmoScan outperforms baselines in interviewing skills, as validated by automated ratings and human evaluations. This work highlights the importance of scalable data-generative pipelines for developing effective mental health LLM tools. Liu, Gao et al. develop EmoScan, an LLM-based system to screen for emotional disorders. Findings reveal that EmoScan effectively screens for emotional disorders with high accuracy and can provide detailed explanations, enhancing transparency and trust in automated mental health assessments. Depression and anxiety are common but often hard to detect. This study explores the potential of artificial intelligence (AI) for identification. We used a type of advanced AI that understands and writes human-like text, known as a large language models (LLMs), to automatically create a large collection of realistic conversations between a psychiatrist and a client. We named this collection of simulated interviews “PsyInterview”. Using this data, we trained a new AI system called EmoScan to capture the signs of emotional disorders. EmoScan can screen for depression and anxiety by having a short, text-based conversation with a person. Our evaluation showed that EmoScan is more accurate at identifying these signs and explaining its reasoning compared to other popular AIs. It also conducts high-quality interviews. This work shows how LLMs can help improve mental health care by making screening faster and more accessible, especially where expert resources are limited.