Imagine if AI could listen to therapy sessions and accurately detect signs of anxiety or depression. It's a compelling idea, and researchers are exploring the potential of Large Language Models (LLMs) like those powering ChatGPT to do just that. A recent study from Stanford and the University of Hawaii analyzed thousands of psychotherapy transcripts, using both cutting-edge LLMs and traditional machine learning methods to see if these tools could reliably identify these mental health conditions. Surprisingly, the results weren't as groundbreaking as you might think. While the idea of AI analyzing complex conversations is exciting, it turns out these advanced models didn't outperform simpler, more established machine learning techniques. In fact, even non-expert humans were often just as good at spotting anxiety and depression in the transcripts. Why the struggle? One reason might be the sheer length of these conversations. LLMs have limits on how much text they can process at once, which could make it difficult for them to pick up on subtle emotional cues spread throughout a lengthy session. Another challenge is that diagnosing these conditions requires a deep understanding of human nuance and subjectivity, something current AI models still haven't quite mastered. This research points to the importance of tempering expectations about what AI can realistically achieve in this complex domain. While LLMs show promise, they're not quite ready to replace human expertise. However, the future holds exciting possibilities. Researchers are exploring techniques to help LLMs better understand long conversations and consider how these models could assist mental health professionals in making more informed diagnoses. The goal isn't to replace human therapists, but to find ways that AI can augment their work and improve mental healthcare for everyone.