Harrison.rad.1 has demonstrated remarkable performance, excelling in radiology examinations designed for human radiologists and outperforming other foundational models in benchmarks. Specifically, it surpasses other foundational models on the challenging Fellowship of the Royal College of Radiologists (FRCR)


Additionally, when assessing Harrison.rad.1 using the VQA-Rad benchmark, a dataset of clinically generated visual questions and answers on radiological images, Harrison.rad.1 achieved an impressive 82% accuracy on closed questions, outperforming other leading foundational models. Similarly, when evaluated on RadBench, a comprehensive and clinically relevant open-source dataset developed by Harrison.ai, the model achieved an accuracy of 73%, the highest among its peers2.
Building on the efficacy, accuracy, and effectiveness that has been achieved through Harrison’s existing Annalise line of products, Harrison.ai wants to collaborate to speed up the development of further AI products in healthcare to help expand capacity and improve patient outcomes.
