ClinicalBench
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Related Articles from SNS
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
arXiv:2411.06469v2 Announce Type: replace Abstract: Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is: Can LLMs beat traditional ML models in clinical prediction?
CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation
Announce Type: new Abstract: Clinical order generation serves as a critical bridge between clinical decision-making and real-world practice, translating medical decisions into concrete and executable orders. Existing agents mainly focus on coarse-grained decisions and overlook the fine-grained, executable information required for clinical orders. To address this gap, we propose CAREAgent, an agent for clinical order generation.