The need for an AI regulatory sandbox in health: lessons from a simulation exercise in a hospital

Abstract Issue Public healthcare systems face challenges related to quality, access, and efficiency of care. AI is increasingly being explored as a possible solution. However, clinical implementation remains limited due to complex and fragmented regulations. The AI Act proposes AI Regulatory Sandboxes (AI-RS) for supervised experimentation with AI, fostering responsible innovation, and clarifying regulatory pathways. Still, its practical implementation in healthcare remains unclear. Description As a first step for developing a health AI-RS, we ran an AI-RS sandbox simulation at the University Medical Center Groningen (UMCG) to test the usefulness and feasibility of implementing an AI-RS within an academic hospital. The study aimed to identify regulatory challenges related to AI as a medical device, evaluate whether an AI-RS could assist, and determine its implementation requirements. Twenty experts across regulatory, legal, clinical, and technical/research fields conducted a simulation using two cases: proton therapy support and fracture prediction tools. Results The use cases identified overlapping regulatory challenges. Most were linked to unclarity around compliance with the Medical Device Regulation and AI Act (e.g., uncertainty around updating and monitoring AI models). Others included CE certification, explainability, and integration with electronic health records. The group concluded that it is premature to establish an AI-RS in the UMCG without clearer guidelines and a mature AI compliance pipeline. Several challenges could be addressed within the existing in-house regulatory and data infrastructure, while others require more regulatory clarity. Lessons AI-RS could support responsible innovation, but an internal AI compliance infrastructure and regulatory dialogue may be preferred. An AI-RS may be of practical relevance nationally for regulatory learning in healthcare and could benefit small and medium enterprises lacking access to data and research infrastructure. Key messages • Despite the AI Act mandating AI-RS, implementation remains unclear. A simulation showed many regulatory challenges can be addressed internally with clear guidance and an AI compliance pipeline. • There is an opportunity for regulatory learning in healthcare with AI-RS at a national level. This could be particularly beneficial for SMEs lacking access to clinical data and infrastructure.

Institutions

Publication Details

Journal
European Journal of Public Health
Published
2025-10-01
DOI
https://doi.org/10.1093/eurpub/ckaf161.388
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The need for an AI regulatory sandbox in health: lessons from a simulation exercise in a hospital

European Journal of Public Health
Artificial Intelligence in Healthcare and Education
article

The need for an AI regulatory sandbox in health: lessons from a simulation exercise in a hospital

article en

Abstract

Abstract Issue Public healthcare systems face challenges related to quality, access, and efficiency of care. AI is increasingly being explored as a possible solution. However, clinical implementation remains limited due to complex and fragmented regulations. The AI Act proposes AI Regulatory Sandboxes (AI-RS) for supervised experimentation with AI, fostering responsible innovation, and clarifying regulatory pathways. Still, its practical implementation in healthcare remains unclear. Description As a first step for developing a health AI-RS, we ran an AI-RS sandbox simulation at the University Medical Center Groningen (UMCG) to test the usefulness and feasibility of implementing an AI-RS within an academic hospital. The study aimed to identify regulatory challenges related to AI as a medical device, evaluate whether an AI-RS could assist, and determine its implementation requirements. Twenty experts across regulatory, legal, clinical, and technical/research fields conducted a simulation using two cases: proton therapy support and fracture prediction tools. Results The use cases identified overlapping regulatory challenges. Most were linked to unclarity around compliance with the Medical Device Regulation and AI Act (e.g., uncertainty around updating and monitoring AI models). Others included CE certification, explainability, and integration with electronic health records. The group concluded that it is premature to establish an AI-RS in the UMCG without clearer guidelines and a mature AI compliance pipeline. Several challenges could be addressed within the existing in-house regulatory and data infrastructure, while others require more regulatory clarity. Lessons AI-RS could support responsible innovation, but an internal AI compliance infrastructure and regulatory dialogue may be preferred. An AI-RS may be of practical relevance nationally for regulatory learning in healthcare and could benefit small and medium enterprises lacking access to data and research infrastructure. Key messages • Despite the AI Act mandating AI-RS, implementation remains unclear. A simulation showed many regulatory challenges can be addressed internally with clear guidance and an AI compliance pipeline. • There is an opportunity for regulatory learning in healthcare with AI-RS at a national level. This could be particularly beneficial for SMEs lacking access to clinical data and infrastructure.

European Journal of Public HealthVol. 35(Supplement_4)
University Medical Center Groningen (NL), Hanze University of Applied Sciences (NL)
Industry, innovation and infrastructure
Openalex Percentile: Top 51%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.