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AGI最难一战,竟在医院!中国AI登上Science,医生不怕失业还催着上线

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Merged summary

TL;DR - Alibaba DAMO Academy’s open-source RADAR model uses vision-language learning to detect 146 conditions across 18 abdominal structures from CT scans. Reported multi-hospital results suggest general-purpose imaging AI can improve diagnostic sensitivity and reading speed while supporting, rather than replacing, radiologists.

  • RADAR achieved a mean AUC of 0.913 on nearly 39,000 internal cases, 0.895 on more than 24,000 cases from eight external hospitals, and 0.904 on 27,000 emergency cases.
  • Its organ-level fine-grained alignment pairs individual CT organs with corresponding report text, avoiding expensive slice-by-slice annotations and reducing irrelevant signals.
  • In comparisons involving 26 radiologists, RADAR outperformed 23; AI assistance raised clinicians’ sensitivity by about 10% and reduced average reading time by over 30%.
  • The model, code, and framework are open source, and the approach may transfer to other imaging modalities such as MRI, PET, and ultrasound.

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AGI最难一战,竟在医院!中国AI登上Science,医生不怕失业还催着上线

量子位 克雷西 2026-09-18 doi:10.1126/science.aec6129
Public signals OpenAlex citations 0 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:18:27.755868 UTC

TL;DR - Alibaba DAMO Academy’s open-source RADAR model uses vision-language learning to detect 146 conditions across 18 abdominal structures from CT scans. Reported multi-hospital results suggest general-purpose imaging AI can improve diagnostic sensitivity and reading speed while supporting, rather than replacing, radiologists.

  • RADAR achieved a mean AUC of 0.913 on nearly 39,000 internal cases, 0.895 on more than 24,000 cases from eight external hospitals, and 0.904 on 27,000 emergency cases.
  • Its organ-level fine-grained alignment pairs individual CT organs with corresponding report text, avoiding expensive slice-by-slice annotations and reducing irrelevant signals.
  • In comparisons involving 26 radiologists, RADAR outperformed 23; AI assistance raised clinicians’ sensitivity by about 10% and reduced average reading time by over 30%.
  • The model, code, and framework are open source, and the approach may transfer to other imaging modalities such as MRI, PET, and ultrasound.
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