A systematic catalog of 89 clinical mental health datasets and 16 synthetic datasets, with a gap analysis on access, culture, and modality.
Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities
1 Pith paper cite this work, alongside 1 external citations. Polarity classification is still indexing.
1
Pith paper citing it
1
external citations · Pith
abstract
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.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Comprehensive Review of Datasets for Clinical Mental Health AI Systems
A systematic catalog of 89 clinical mental health datasets and 16 synthetic datasets, with a gap analysis on access, culture, and modality.