Radar: An Expert-Level Generalist AI for Abdominal CT Diagnosis
RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis RADAR is a generalist vision-language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs, learning directly from clinical reports without manual annotation. RADAR provides a scalable and versatile framework for radiology AI, demonstrating expert-level performance across both routine and complex clinical tasks. Setup Create a conda environment and install the required dependencies: conda create -n radar python=3.10 conda activate radar pip install -r requirements.txt HuggingFace The pre-trained checkpoints and supporting files are available on HuggingFace.
- ▪RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis RADAR is a generalist vision-language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs, learning directl
- ▪RADAR provides a scalable and versatile framework for radiology AI, demonstrating expert-level performance across both routine and complex clinical tasks.
- ▪Setup Create a conda environment and install the required dependencies: conda create -n radar python=3.10 conda activate radar pip install -r requirements.txt HuggingFace The pre-trained checkpoints and supporting files are available on Hug
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| Original publisher | GitHub |
| Canonical URL | https://github.com/alibaba-damo-academy/damo-radar |
| Publication time | Sat, 19 Sep 2026 00:16:12 +0000 |
| Retrieval time | 2026-09-19T00:28:45.639Z |
| Last seen | 2026-09-19T00:28:45.639Z |
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| Summary source text | contentText |
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| Cluster | TMZO9odz3Pw1 · 1 stories |
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| Publisher visit | Yes — open original |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis RADAR is a generalist vision-language model trained on over 400,000 contrast-enhanced abdominal CT examinations with 15 million anatomy-aware image–text pairs, learning directly from clinical reports without manual annotation. RADAR provides a scalable and versatile framework for radiology AI, demonstrating expert-level performance across both routine and complex clinical tasks. Setup Create a conda environment and install the required dependencies: conda create -n radar python=3.10 conda activate radar pip install -r requirements.txt HuggingFace The pre-trained checkpoints and supporting files are available on HuggingFace. For convenience, we have provided the demo nifty, and predicted results in CSV format in this repo.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.