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Updated: Jan 15, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Towards privacy-aware mental health AI models
Aishik Mandal1,2, Tanmoy Chakraborty3,4, Iryna Gurevych5,6
1Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt, Darmstadt, Germany.
Abstract:
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.
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