Imagine an AI that can understand the complexities of mental health, not just in English but across multiple languages, offering support and insights to those who need it most. Researchers are exploring this frontier, investigating how Large Language Models (LLMs) can analyze text and potentially help assess the severity of mental health conditions. A new study tackled this challenge by creating a novel multilingual dataset, translating existing English mental health datasets into six other languages (Greek, Turkish, French, Portuguese, German, and Finnish). This allowed them to test how well LLMs like GPT and Llama could perform across different linguistic contexts. The results revealed some fascinating patterns: LLMs performed differently depending on the language and the nuances in how mental health is expressed. For instance, the models showed inconsistencies in recognizing depression severity levels. This highlights the challenge of capturing the subtle ways people communicate their mental health experiences across diverse cultures. The research also found that the performance of LLMs varied depending on the translated language. Interestingly, some languages saw improved performance compared to English, which suggests that translation might help in highlighting underlying patterns. However, the overall message is clear: LLMs alone aren’t ready to take over mental health assessment. The risks of misdiagnosis are real. This research underscores the importance of human oversight in any healthcare setting and calls for further research on the potential benefits and risks of using AI in mental health. Future research could explore ways to improve the accuracy and reliability of LLM-based mental health tools across a broader range of languages, including low-resource ones, where digital mental health support is often scarce. The potential of AI to assist in this critical area is immense but requires careful consideration of both the technology and its ethical implications.