Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914003408650240 |
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| author | Mandal, Aishik Chakraborty, Tanmoy Gurevych, Iryna |
| author_facet | Mandal, Aishik Chakraborty, Tanmoy Gurevych, Iryna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities Mandal, Aishik Chakraborty, Tanmoy Gurevych, Iryna Computation and Language Artificial Intelligence 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. |
| title | Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.00451 |