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Radiology-specific foundation model
Introducing Harrison.rad.1, the latest frontier in radiology-specific foundational models. Harrison.rad.1 outperforms other frontier models by ~2x in the FRCR 2B Rapids exam.
Other competing models, including OpenAI’s GPT-4o, Microsoft’s LLaVA-Med, Anthropic’s Claude 3.5 Sonnet and Google’s Gemini 1.5 Pro, mostly scored below 30**, which is statistically no better than random guessing. Revolutionary Capabilities Harrison.rad.1 presents a wealth of potential opportunities in global healthcare, such as improving clinical excellence and quality, and providing non-medical use cases too, such as accelerating the development of AI products. General purpose Large Language Models (LLMs) are powerful, but their broad, generic focus renders them less suited to critical applications where accuracy is of paramount importance.
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