Delays between CE mark and FDA regulatory approval of AI-enabled software for radiology

Abstract Artificial intelligence (AI)-enabled radiology software requires regulatory authorisation before marketing in Europe (EU) and the United States, but cross-jurisdictional approval sequencing remains poorly characterised. We analysed 239 AI-enabled radiology software devices with CE marking and/or FDA clearance from a publicly available European database (Health AI Register) and FDA sources using Kaplan–Meier methods and Cox models. Overall, 128 devices were CE-marked only, 95 were CE-marked before FDA clearance, and 16 were FDA-cleared before CE marking. Among dual-authorised devices, CE-first devices had longer median lags to second authorisation than FDA-first devices (17.5 months [IQR, 8.0–35.5] versus 3.5 [IQR, 1.0–11.3]; p < 0.001). Radiograph interpretation software was associated with longer time to second authorisation (hazard ratio, 0.42; 95% CI, 0.21–0.84; p = 0.01), while EU Class IIa classification (4.99; 1.77–14.04; p < 0.01) and peer-reviewed evidence (1.44; 0.83–2.49; p = 0.19) showed shorter intervals. These findings highlight persistent transatlantic asymmetry and underscore the need for greater regulatory coordination and transparency.

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2026-08-22
DOI
https://doi.org/10.1038/s41746-026-03165-5
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

Delays between CE mark and FDA regulatory approval of AI-enabled software for radiology

Daniel Windecker, Yijun Ren, Alan G. Fraser, George CM Siontis et al.
npj Digital Medicine
Artificial Intelligence in Healthcare and Education
article

Delays between CE mark and FDA regulatory approval of AI-enabled software for radiology

Daniel Windecker, Yijun Ren, Alan G. Fraser, George CM Siontis, Christoph Gräni, Isaac Shiri, Enrico G. Caiani
article en

Abstract

Abstract Artificial intelligence (AI)-enabled radiology software requires regulatory authorisation before marketing in Europe (EU) and the United States, but cross-jurisdictional approval sequencing remains poorly characterised. We analysed 239 AI-enabled radiology software devices with CE marking and/or FDA clearance from a publicly available European database (Health AI Register) and FDA sources using Kaplan–Meier methods and Cox models. Overall, 128 devices were CE-marked only, 95 were CE-marked before FDA clearance, and 16 were FDA-cleared before CE marking. Among dual-authorised devices, CE-first devices had longer median lags to second authorisation than FDA-first devices (17.5 months [IQR, 8.0–35.5] versus 3.5 [IQR, 1.0–11.3]; p < 0.001). Radiograph interpretation software was associated with longer time to second authorisation (hazard ratio, 0.42; 95% CI, 0.21–0.84; p = 0.01), while EU Class IIa classification (4.99; 1.77–14.04; p < 0.01) and peer-reviewed evidence (1.44; 0.83–2.49; p = 0.19) showed shorter intervals. These findings highlight persistent transatlantic asymmetry and underscore the need for greater regulatory coordination and transparency.

npj Digital Medicine
University of Bern (CH), Bern University of Applied Sciences (CH), University Hospital of Wales (GB), University Hospital of Bern (CH), IRCCS Istituto Auxologico Italiano (IT), Institute of Social and Preventive Medicine (CH), Politecnico di Milano (IT)
Peace, Justice and strong institutions
Openalex Percentile: Top 13%
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.