RadBench is a radiology benchmark framework developed by Harrison.ai. It is designed to evaluate the performance of Harrison.ai's foundational radiology model, harrison.rad.1, against other competitive models in the field. The framework employs a rigorous evaluation methodology across three distinct datasets to ensure the models are thoroughly assessed for clinical relevance, accuracy, and case comprehension. These datasets are:
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RadBench Dataset: A new visual question-answering dataset designed by Harrison.ai to benchmark radiology models.
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VQA-RAD Dataset: A visual question-answering dataset for radiology, available at Nature Datasets.
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Fellowship of the Royal College of Radiologists (FRCR) 2B Examination: Curated for the Fellowship of the Royal College of Radiologists (FRCR) Rapids 2B exam, obtained from third parties to ensure fairness in our evaluation process.
To launch mkdocs locally, follow these instructions:
- Create a Python environment:
python3 -m venv .venv
. .venv/bin/activate- Install the dependencies:
make install- Start the serving endpoint:
make serve