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Found 4 papers, 2 papers with code
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time.
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification
Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features.
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems
In addition, we experiment with different transfer learning strategies to effectively adapt these pretrained models to new tasks and healthcare systems.
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS).
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