Towards privacy-aware mental health AI models

Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Recent advances in artificial intelligence, particularly natural language processing and multimodal methods, offer promise for detecting and addressing mental disorders. However, these innovations also introduce privacy concerns. Here we examine these challenges and propose solutions, including anonymization, synthetic data and privacy-preserving training, while outlining frameworks for privacy–utility trade-offs, aiming to advance reliable, privacy-aware artificial-intelligence tools that support clinical decision-making and improve mental health outcomes. In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility trade-offs. They suggest a pipeline for developing privacy-aware mental health AI systems.