An open vision-language model for diverse medical applications
The results show that fine-tuning MedGemma can be more effective than fine-tuning the base Gemma 3 model for medical tasks, particularly in the setting of limited training data.
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The results show that fine-tuning MedGemma can be more effective than fine-tuning the base Gemma 3 model for medical tasks, particularly in the setting of limited training data.
KMGen is introduced, the first end-to-end framework that fully automates KM curve extraction at accuracy competitive with human-guided tools, and generates synthetic per-patient AE trajectories from public trial registry records.
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