BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text

1. 1Collect and preprocess large-scale electronic health records (EHRs) data
2. 2Develop benchmarking tasks that reflect real-world clinical practice
3. 3Evaluate large language models on these tasks to assess their understanding of clinical text
4. 4Analyze results to identify areas for improvement in language model development

Data scientists and AI engineers on healthcare teams benefit from BRIDGE as it evaluates the performance of large language models on real-world clinical data, informing their development and application

💡 Benchmarking large language models on real-world clinical data is crucial for their reliable application in healthcare

🏥 New benchmark for large language models in healthcare: BRIDGE evaluates models on real-world clinical text 📊