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This paper introduces ChristBERT, a family of domain-specific RoBERTa-based language models for German clinical NLP, and evaluates three domain adaptation strategies (continued pre-training, pre-training from scratch, and vocabulary adaptation) on medical named entity recognition and text classification tasks, achieving state-of-the-art results.
This paper presents a specialty-specific medical language model for extracting information from clinical narratives about immune-mediated and infectious diseases, using a BiLSTM-CNN-Char architecture trained on a curated corpus of 371 case reports, achieving an F1 score of 0.89.
Leanly_AI is a specialized large language model developed by Fuzhou University hospitals to provide evidence-informed psychological support for patients undergoing clinical weight management. The model integrates population health data to address obesity-related emotional challenges while maintaining clinical interpretability and safety.