OpenRad: a Curated Repository of Open-access AI models for Radiology

2026-03-02

AI Summary

This work addresses the fragmentation of radiology AI models across disparate platforms, which hinders their discoverability, reproducibility, and clinical translation. To overcome this, we present the first structured, open-access model repository encompassing all imaging modalities and radiology subspecialties. The repository integrates a locally deployed large language model (gpt-oss:120b) to automatically extract metadata according to the RSNA AI Roadmap JSON schema, followed by manual curation by ten domain experts. Extraction stability was evaluated using Levenshtein similarity. The library currently includes approximately 1,700 vetted models—predominantly MRI-based (621 models)—originating mainly from the U.S. and China, with architectures primarily based on CNNs and Transformers. The platform supports multidimensional filtering and real-time analytics, substantially enhancing model accessibility, standardization, and community-driven development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The rapid developments in artificial intelligence (AI) research in radiology have produced numerous models that are scattered across various platforms and sources, limiting discoverability, reproducibility and clinical translation. Herein, OpenRad was created, a curated, standardized, open-access repository that aggregates radiology AI models and providing details such as the availability of pretrained weights and interactive applications. Retrospective analysis of peer reviewed literature and preprints indexed in PubMed, arXiv and Scopus was performed until Dec 2025 (n = 5239 records). Model records were generated using a locally hosted LLM (gpt-oss:120b), based on the RSNA AI Roadmap JSON schema, and manually verified by ten expert reviewers. Stability of LLM outputs was assessed on 225 randomly selected papers using text similarity metrics. A total of 1694 articles were included after review. Included models span all imaging modalities (CT, MRI, X-ray, US) and radiology subspecialties. Automated extraction demonstrated high stability for structured fields (Levenshtein ratio > 90%), with 78.5% of record edits being characterized as minor during expert review. Statistical analysis of the repository revealed 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 OpenRad web interface enables model discovery via keyword search and filters for modality, subspecialty, intended use, verification status and demo availability, alongside live statistics. The community can contribute new models through a dedicated portal. OpenRad contains approx. 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.

Problem

• radiology
• AI models
• discoverability
• reproducibility
• clinical translation

Innovation

• OpenRad
• radiology AI models
• large language model
• standardized metadata
• open-access repository