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An autonomous AI agent integrating Reasoning and Acting with Retrieval-Augmented Generation with Retrieval-Augmented Generation is developed to classify 10,211 Human Phenotype Ontology terms, enabling standardized panel design by providing reliable, automated classification supported by direct evidence.
Tailoring medical image segmentation models to dataset-specific requirements can boost performance by over 10% compared to traditional architecture-first approaches.
