SatIR uses SMT solvers and LLMs to formalize clinical constraints and achieves 32-72% more relevant trial matches per patient than TrialGPT on a set of 59 patients and 3,621 trials.
Matching patients to clinical trials with large language models
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This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.
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Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching
SatIR uses SMT solvers and LLMs to formalize clinical constraints and achieves 32-72% more relevant trial matches per patient than TrialGPT on a set of 59 patients and 3,621 trials.
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Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence
This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.