Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities
It tackles privacy challenges in AI for mental health, which is crucial for improving accessibility and outcomes, but is incremental as it reviews existing methods.
The paper addresses privacy risks in AI models for mental health diagnostics, proposing solutions like anonymization and privacy-preserving training to advance reliable tools for clinical decision-making.
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Advances in artificial intelligence, particularly natural language processing and multimodal methods, offer promise for detecting and addressing mental disorders, but raise critical privacy risks. This paper examines these challenges and proposes solutions, including anonymization, synthetic data, and privacy-preserving training, while outlining frameworks for privacy-utility trade-offs, aiming to advance reliable, privacy-aware AI tools that support clinical decision-making and improve mental health outcomes.