2026-10-11 16:38 UTC

Alibaba DAMO Academy claims its released RADAR vision-language model achieves expert-level abdominal CT diagnosis using report-derived training, potentially providing a reusable checkpoint and training pipeline for medical-imaging research.

state: watchingheat: highuncertainty: highconvergesscott: lowmedical-ai open-models vision-modelsAlibaba DAMO Academy
Surfaced 2026-09-19T10:23:57Z — RADAR claims expert-level abdominal CT performance after training on over 400,000 examinations and 15 million anatomy-aware image–text pairs — Cross-platform circulation now meets the broad-spread attention threshold, warranting high heat despite unchanged technical evidence and low direct relevance to Scott. The growing audience does not independently substantiate expert-level diagnosis, checkpoint usability, or reproducibility.

What is this?

RADAR (Rapid Abdominal Diagnosis with AI and Radiology) is a vision-language model from Alibaba DAMO Academy and hospital collaborators for interpreting contrast-enhanced abdominal CT scans. The supplied reports describe training on 424,911 examinations by linking anatomical structures to radiology-report descriptions, yielding 1.5 million image–text pairs and more than 15 million anatomy-specific pairs; reported evaluation covers 146 findings across 18 abdominal structures, with an average AUC of 0.913 in nearly 40,000 examinations. News coverage says the model is open-sourced, and a Zenodo record confirms an archived source-code package, but the snippets do not establish checkpoint availability, licensing, or training-pipeline reproducibility. “Expert-level” is a reported research claim, not independently established here; the supplied Science news release lists publication on September 17, 2026.

Why it matters to Scott

RADAR’s conversion of existing radiology reports into training pairs parallels Scott’s Salesforce fine-tuning-data factory: repurposing domain records into model supervision, not compiling an agent-maintained wiki. This is another example of that pattern rather than an actionable extension: the hits establish no CT-imaging project for Scott, and checkpoint availability, licensing and pipeline reproducibility remain unconfirmed.
dev:project.redditradar:concept.model-trainingradar:concept.multimodal-models
queries asked of Scott's wikis
  • domain model training from existing reports and unstructured records
  • fine-grained multimodal alignment structured knowledge extraction
  • open model checkpoints licensing reproducible training pipelines
  • specialist versus generalist AI expert-level evaluation
  • medical imaging AI clinical validation projects

Measured heat

now 0 pts/hpeak 0 pts/hcomments 0/hpeers p14momentum: steady3 platformsage 544h
points/hour across evidence · reading as of 2026-10-12 02:59:37.977291+11:00 · deterministic, not a model opinion

How the heat travelled

09-19 00:22 (minted)⭐ origin echo-reconstructedRADAR claims expert-level abdominal CT performance after training on over 400,000 examinations and 15 million anatomy-aware image–text pairs
Alibaba DAMO Academy on github (echo) · attributed from hn.story.49761981 · published time unknown
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09-18 23:54first on hacker news · published · lag ?Alibaba open-sources AI model that can detect cancer and nearly 150 conditions
yogthos
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09-19 02:08first on r/LocalLLaMA · published · lag ?Alibaba open-sources medical AI model that can detect cancer and nearly 150 conditions
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09-18 23:54amplified on hacker newshn.story.49761840
yogthos
peak 153 · 22 comments · 18% of case engagement
09-19 00:16amplified on hacker newshn.story.49761981
rguiscard
peak 3 · 0 comments · 0% of case engagement
09-19 02:08amplified on r/LocalLLaMA 👑reddit.post.1wk9fag
giveen
peak 1312 · 83 comments · 81% of case engagement
09-19 00:21our radar first saw it · lag ?discovery anchor: hn.story.49761981—
09-19 10:23reached heat=high · lag ? · via ledger——
pace: p93 vs 1032 stories at the 336h mark (now 544h old) — ahead of deepseek-v4-pro-soft-retirement (1.1x), behind military-ai-false-ship-intelligence (1.0x)

Evidence (4) — ⭐ canonical anchor

sourceobjectauthorscorecomments
🟧 hnRadar: An Expert-Level Generalist AI for Abdominal CT Diagnosis
Retrieved article excerpt

Open article · Retrieved 2026-09-19T00:22:12.723402+00:00

# RADAR: An Expert-Level Generalist AI for Abdominal CT Diagnosis

[Paper](https://www.science.org/doi/10.1126/science.aec6129)
[GitHub](https://github.com/alibaba-damo-academy/damo-radar)
[Zenodo](https://zenodo.org/records/21271172)
[Hugging Face](https://huggingface.co/radar-generalist)
[License](https://creativecommons.org/licenses/by-nc-sa/4.0/)

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.

[RADAR Overview](https://github.com/alibaba-damo-academy/damo-radar/blob/main/docs/radar_fig0.png)

---

## 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](https://huggingface.co/radar-generalist).
- For convenience, we have provided the demo nifty, and predicted results in CSV format in this repo. The supporting files required for the inference demo and training can be downloaded from HuggingFace.
- Download via scripts: We provide two helper scripts under `download_scripts/` to fetch the required files from HuggingFace:

```
cd download_scripts
# Download model checkpoints and support files into ckpt/
python download_checkpoints.py
# Download auxiliary data (processed masks)
python download_auxiliary_data.py
```

---

## Zenodo

Code can also be archived on [Zenodo](https://zenodo.org/records/21271172).

---

## Documentation

For detailed instructions, please refer to the following guides:

| Guide | Description |
| --- | --- |
| [Training](https://github.com/alibaba-damo-academy/damo-radar/blob/main/docs/TRAINING.md) | Train RADAR/RADAR+ from scratch or fine-tune on MERLIN data; inference and evaluation are also included. |
| [Inference](https://github.com/alibaba-damo-academy/damo-radar/blob/main/docs/INFERENCE.md) | 1. An inference demo with a radar pre-trained checkpoint on RAD-CT, and 2. Inference and evaluation of radar performance on the external MERLIN test set. |
| [Preprocess](https://github.com/alibaba-damo-academy/damo-radar/blob/main/docs/PREPROCESS.md) | Image/mask and radiology report preprocessing code, which can be used to process the MERLIN data or your own custom data. |



---

## Acknowledgements

This project is built upon the following open-source projects:

- [LAVIS](https://github.com/salesforce/LAVIS) (BSD 3-Clause License)
- [nnU-Net](https://github.com/MIC-DKFZ/nnUNet) (Apache License 2.0)
- [MONAI](https://github.com/Project-MONAI/MONAI) (Apache License 2.0)
- [3D-ResNets-PyTorch](https://github.com/kenshohara/3D-ResNets-PyTorch) (MIT License)

---

## License

This project is released under the [Apache License 2.0](https://github.com/alibaba-damo-academy/damo-radar/blob/main/LICENSE).

Portions of the code are derived from third-party open-source projects that are distributed under their own licenses (see the [Acknowledgements](https://github.com/alibaba-damo-academy/damo-radar#acknowledgements) above). Their original license texts are retained in [`THIRD_PARTY_LICENSES.md`](https://github.com/alibaba-damo-academy/damo-radar/blob/main/THIRD_PARTY_LICENSES.md).

---

## Citation

If you find RADAR useful in your research, please cite our paper:

```
@article{damo-radar-2026,
    author = {Qi Zhang and Jianpeng Zhang and Weiwei Cao and Zilin Lu and Wanxing Chang and Haonan Ding and Cao Chen and Zhi Li and Xing Xue and Sinuo Wang and Shaoteng Zhang and Yutong Xie and Yong Xia and Qi Wu and Zhongyi Shui and Xi Li and Zhilin Zheng and Yanjie Zhou and Tony C.W. Mok and Yingda Xia and Hongkan Wang and Xianghua Ye and Tao Ma and Jie Peng and Xiaoguang Wang and Jian Ding and Yuming Gao and Huazhen Ye and Yiping Liu and Dongjie Chen and Zhaomin Ni and Jianwen Ning and Wei Zhang and Jian Liu and Chaohui Yu and Shenghong Ju and Jianfeng Zhang and Wenbo Xiao and Ling Zhang and Tingbo Liang },
    title = {An expert-level generalist AI for abdominal CT diagnosis},
    journal = {Science},
    volume = {393},
    number = {6817},
    pages = {eaec6129},
    year = {2026},
    doi = {10.1126/science.aec6129},
    URL = {https://www.science.org/doi/abs/10.1126/science.aec6129}
}
```
rguiscard30
🟧 echo.github ⭐RADAR claims expert-level abdominal CT performance after training on over 400,000 examinations and 15 million anatomy-aware image–text pairsAlibaba DAMO Academy——
🟧 hnAlibaba open-sources AI model that can detect cancer and nearly 150 conditionsyogthos15322
🟠 redditAlibaba open-sources medical AI model that can detect cancer and nearly 150 conditions
LocalLLaMA
giveen131283

Interpretation history

Decision trace