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Advancing neurotech justice in youth digital mental health: insights from an interdisciplinary and cross-generational workshop

Abstract:
Researchers and clinicians are increasingly looking to leverage artificial intelligence (AI) and digital tools to improve psychiatric care. Of particular promise is addressing the youth mental health crisis. Yet, the introduction of AI-enabled digital technologies for psychiatric treatment of young adults raises a host of ethical, legal, and societal issues (ELSI). To provide guidance in addressing these issues, we convened a two-day meeting at the Radcliffe Institute for Advanced Study at Harvard University: Advancing Neurotech Justice in Mental Health: Insights from an Interdisciplinary and Cross-Generational Workshop. The meeting brought together a diverse cohort of 17 experts and 5 students from various fields and different countries. In partnership with the MIT Critical Data team, the workshop engaged participants in an interactive Prompt-a-Thon to explore first-hand the potential benefits, biases, and harms related to the use of Large Language Model chatbots in mental health care. This Perspective reports on five principles of digital psychiatry deployment that the workshop participants determined to be the most essential: ensuring accuracy, remaining human-centric, promoting just access, protecting privacy, and providing transparency. We place these five principles within a “Neurotech Justice” framework and discuss how guardrails can be built to promote neurotech justice in digital psychiatry.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s44277-025-00052-x

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0009-0004-9063-7984
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Role:
Author
ORCID:
0009-0001-8696-3259
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Role:
Author
ORCID:
0000-0002-1907-9471
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Role:
Author
ORCID:
0000-0003-1264-6522
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Role:
Author
ORCID:
0000-0001-6712-6626


Publisher:
Springer Nature [academic journals on nature.com]
Journal:
NPP—Digital Psychiatry and Neuroscience More from this journal
Volume:
4
Issue:
1
Article number:
7
Publication date:
2026-04-08
Acceptance date:
2025-12-08
DOI:
EISSN:
2948-1570
ISSN:
2948-1570


Language:
English
Keywords:
Subtype:
Review
Pubs id:
2405997
Local pid:
pubs:2405997
Source identifiers:
3934299
Deposit date:
2026-04-09
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

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