Objectives To create and evaluate OpenRad (<a href="https://konstvr.github.io/OpenRad/index.html">https://konstvr.github.io/OpenRad/index.html</a>), 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 methods Retrospective 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. Results The 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. Conclusion OpenRad 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 <Emphasis Type="BoldItalic">Question</Emphasis> Current repositories of AI models in radiology are limited, fragmented and include models that are not readily available for use. <Emphasis Type="BoldItalic">Findings</Emphasis> OpenRad, a curated repository, includes ~1700 open-access, standardized radiology AI models with verified code repositories. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> OpenRad enables the radiology community to reliably access AI models with readily available code, weights and demos. Graphical Abstract

OpenRad: a curated repository of open-access AI models for radiology

期刊论文
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发表日期：

2026年5月28日

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Konstantinos Vrettos,
Galini Papadaki,
Emmanouil Brilakis,
Matthaios Triantafyllou,
Dimitrios Leventis,
Despina Staraki,
Maria Mavroforou,
Eleftherios Tzanis,
Konstantina Giouroukou,
Michail E. Klontzas

摘要

Objectives

To 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 methods

Retrospective 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.

Results

The 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.

Conclusion

OpenRad 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.

Graphical Abstract