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College of Computer Science and Technology, Zhejiang University, Hangzhou, China, These authors contributed equally to this work.
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DeepBD outperforms traditional variant prioritization tools by integrating LLMs and specialized evidence modules, achieving a Recall@10 of 92.9% on a large cohort of genetic cases.
EDIT reveals that targeted interventions based on internal model diagnostics can dramatically enhance LLM grading accuracy, outperforming conventional methods.
