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This paper presents an empirical study of inference-time gating in a production-scale clinical NLP pipeline using Llama and MMed-Llama models, showing that learning filtering rules from verifier rejections fails at scale, while ontology-based and evidence-testing filters are effective.
This paper investigates LLM-based metrics for evaluating clinical significance in radiology report generation. It identifies discrimination bias in existing LLM evaluators and proposes training lightweight interpretable metrics to improve the balance between error detection and tolerance of harmless variations.
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.