Can I read Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities on EtoBox?

Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities by Mandal, Aishik; Chakraborty, Tanmoy; Gurevych, Iryna is a scholarly article available to read on EtoBox.

What is Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities about?

Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approaches, such as self-reported questionnaires and psychotherapy sessions, often impose significant burdens on both patients and clinicians, limiting accessibility and efficiency. Recent advances in Artificial Intelligence (AI), particularly in Natural Language Processing and multimodal techniques, hold great potential for recognizing and addressing conditions such as depression, anxiety, bipolar disorder, schizophrenia, and post-traumatic stress disorder. However, privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings. These challenges are amplified in multimodal methods, where personal identifiers such as voice and facial data can be misused. This paper presents a critical and comprehensive study of the privacy challenges associated with developing and deploying AI models for mental health. We further prescribe potential solutions, including data anonymization, synthetic data generation, and privacy-preserving m

Author

Mandal, Aishik; Chakraborty, Tanmoy; Gurevych, Iryna

Published

2025

Language

EN