ArticleEuropean radiology2026
OpenRad: a curated repository of open-access AI models for radiology.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo create and evaluate OpenRad ( https://konstvr.github.io/OpenRad/index.html ), a curated, standardized repository that aggregates open-access radiology artificial intelligence (AI) models enriched with metadata from the corresponding code repositories regarding availability of pretrained weights and interactive applications. MATERIALS AND
methodsRetrospective analysis of literature from PubMed, arXiv, and Scopus until 12/2025 (5239 works). After duplicate removal and relevance screening, 1694 articles describing open-access AI models were processed. Model records were generated using a locally hosted large language model (LLM) (gpt-oss:120b), based on the RSNA AI Roadmap JSON schema, and then manually verified by ten expert reviewers. The stability of LLM outputs was assessed on 225 randomly selected papers using text similarity metrics. A statistical analysis of the collected works was also performed.
resultsThe included 1694 models span all imaging modalities (computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound (US)) and radiology subspecialties. Automated extraction demonstrated high stability for structured fields (Levenshtein ratio > 90%), with 78.5% of edits, during expert review, being minor corrections. Statistical analysis of the repository revealed convolutional neural network (CNN) and transformer architectures as dominant, while MRI was the most commonly used modality (in 621 neuroradiology AI models). Research output was mostly concentrated in China and the United States. The proposed web interface enables model discovery via keyword search and filters for modality, subspecialty, intended use and demo availability, alongside live statistical dashboards. The community can also contribute new models through a dedicated portal.
conclusionOpenRad contains ~1700 open-access, curated radiology AI models with standardized metadata, supplemented with analysis of code repositories, thereby creating a comprehensive, searchable resource for the radiology community. KEY POINTS: Question Current repositories of AI models in radiology are limited, fragmented and include models that are not readily available for use. Findings OpenRad, a curated repository, includes ~1700 open-access, standardized radiology AI models with verified code repositories. Clinical relevance OpenRad enables the radiology community to reliably access AI models with readily available code, weights and demos.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.