This paper explores the potential of leveraging artificial intelligence (AI), particularly natural language processing and multimodal methods, to diagnose and treat mental health disorders, while also addressing the significant privacy risks these approaches pose. Given the resource-intensive and inaccessible nature of existing mental health diagnostic methods, AI-based approaches offer the potential for increased efficiency, but highlight the need to address privacy concerns. This paper presents a framework that balances privacy and usability, including anonymization, synthetic data, and privacy-conscious learning. It explores approaches for developing trustworthy and privacy-preserving AI tools that support clinical decision-making and improve mental health outcomes.
