Cyberphysical Intelligence as AI-Driven CRIS Infrastructure: Adaptive Metadata Generation and Governed Interoperability in Forensic Psychiatry
Author(s)
Renaud, Patrice
Université du Québec en Outaouais
Boukhalfi, Tarik
Université du Québec à Trois-Rivières
Malouin, Mario
Université du Québec en Outaouais
Issue Date
May 22, 2026
Publisher
euroCRIS
Type
Conference Paper
Abstract
This paper proposes Cyberphysical Intelligence (CPS-I) as a new way of thinking about Research Information Systems (CRIS) in forensic psychiatry. Rather than serving solely as repositories of finalized research outputs, CRIS are here approached as active infrastructures capable of supporting both clinical activity and the continuous production of research-relevant data. In this perspective, CPS-I functions as a transformation and representation layer, converting ongoing multimodal cyberphysical streams into structured, derived research information objects.
By combining digital twins, extended reality (XR), and AI-driven semantic enrichment, the framework enables the dynamic generation of metadata while maintaining controlled and traceable forms of interoperability. Particular attention is given to the constraints inherent to forensic psychiatric settings, where ethical, legal, and security considerations shape how data can be structured, accessed, and shared. Within these boundaries, CPS-I aligns with FAIR principles at the level of metadata and derived representations.
More broadly, the framework suggests a shift in how CRIS infrastructures are conceptualized: from static registries toward dynamic, epistemic environments capable of capturing embodied, real-time processes. This evolution remains compatible with CERIF standards, while extending their scope to domains where research unfolds as a continuous, situated, and tightly governed activity.
Description
29 slides.-- Presentation delivered within CRIS2026 session "AI-Enhanced CRIS for Decision Support, Prediction, and Policy".-- Includes extended abstract
URI
https://dspacecris.eurocris.org/handle/11366/9336
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CRIS2026_paper-15_Renaud-Boukhalfi-Malouin_Cyberphysical-Intelligence_extended-abstract.pdf
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Extended abstract
Size
229.09 KB
Format
Adobe PDF
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Name
CRIS2026_Renaud-Boukhalfi-Malouin_slides_CPSI_CRIS.pdf
Description
Presentation
Size
759.21 KB
Format
Adobe PDF
Checksum (MD5)
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Conference(s)