How effective are llms in diagnosing mental health disorders? 

Large Language Models (LLMs) like ChatGPT have shown promising results in diagnosing mental health disorders, albeit with varying degrees of effectiveness across different tasks and conditions. For instance, in text-based mental health classification tasks, such as stress, depression, and suicidality detection, LLMs have demonstrated potential, achieving F1 scores that indicate a significant improvement over baseline models. This suggests that LLMs can effectively identify certain mental health conditions from social media posts, which is a step forward in leveraging technology for mental health diagnostics. However, the effectiveness of LLMs in clinical settings, particularly in tasks like clinical text mining for extracting structured information from healthcare texts, has been less straightforward. Initial attempts to employ ChatGPT directly for tasks such as biological named entity recognition and relation extraction resulted in poor performance. This highlights the challenges LLMs face when applied to specialized, domain-specific tasks without further fine-tuning or adaptation. Moreover, the application of LLMs in diagnosing conditions like dementia has shown that, while LLMs exhibit potential, they currently do not outperform traditional AI tools. This underscores the necessity for further research and development to enhance the capabilities of LLMs in specialized domains such as dementia diagnosis. In the broader context of mental health, LLMs and machine learning models have been explored for various applications, including detection, diagnosis, and treatment support. These technologies offer new avenues for understanding patterns of human behavior and identifying symptoms and risk factors of mental health conditions. Yet, the development of effective ML-enabled applications for real-world mental health contexts is complex and requires addressing numerous challenges. In summary, while LLMs have shown effectiveness in diagnosing certain mental health disorders, their performance varies across different tasks and conditions. The current state of research indicates both the potential and the limitations of LLMs in this domain, highlighting the need for ongoing development and multidisciplinary approaches to fully realize their capabilities.

Evaluation of ChatGPT for NLP-based Mental Health Applications

LLMs, like ChatGPT, show promise in diagnosing mental health disorders with F1 scores of 0.73 for stress, 0.86 for depression, and 0.37 for suicidality detection tasks.

Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems

Machine learning systems show promise in diagnosing mental health disorders by learning behavior patterns, identifying symptoms, and personalizing therapies, but face challenges in practical implementation.

LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation

LLMs, like ChatGPT, show promise in diagnosing mental health disorders by simulating psychiatrist-patient conversations effectively, as demonstrated in the study through collaboration with professionals and evaluation experiments.

An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorder using Machine learning Approach

LLMs are highly effective in diagnosing mental health disorders, achieving 91.00% accuracy in early detection and monitoring, surpassing existing approaches in the field of machine learning.