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RadBench: Radiology Benchmark Framework#

Overview#

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:

  1. RadBench Dataset: A new visual question-answering dataset designed by Harrison.ai to benchmark radiology models.

  2. VQA-RAD Dataset: A visual question-answering dataset for radiology, available at Nature Datasets.

  3. 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.