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.
Harrison.rad.1 excels in the same radiology exams taken by human radiologists, as well as in benchmarks against other foundational models.
The Fellowship of the Royal College of Radiologists (FRCR) 2B Rapids exam is considered one of the leading and toughest certifications for radiologists. Only 40-59% of human radiologists pass on their first attempt. Radiologists who re-attempt the exam within a year of passing score an average of 50.88 out of 60 (84.8%)*.
Harrison.rad.1 scored 51.4 out of 60 (85.67%). 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.***
*British Medical Journal
**Data on file
***As measured by mock examinations, actual FRCR 2B Rapids questions are not publicly available
Accelerating validation and responsible introduction of generative AI in healthcare.
Unmatched Performance
Excels in benchmarks against other foundational models, as well as in standard radiology examinations taken by human radiologists.
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.
A specialised multimodal LLM that prioritises factual correctness and clinical precision.
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. A highly specialised and nuanced function like healthcare requires a specialised model. This is why Harrison.rad.1 was born.
Harrison.rad.1 has several
emergent capabilities:
What’s Harrison.rad.1’s big advantage?
Exclusive Dataset
We have proprietary access to extensive medical imaging data that is representative and diverse, enabling superior model training and accuracy.
Medical Expert Annotation
Our data is annotated at scale by medical specialists, ensuring high-quality training signals that enhance model reliability and clinical relevance.
Specialised Model Architecture
Our model is specifically designed for healthcare data, fine-tuned for clinical usefulness and factual correctness.
Harrison.rad.1 is a significant technological leap towards our end goal of creating dramatically more capacity in radiology. We are making our model available to select collaborators to help accelerate research into validation methods and the responsible integration of this technology into clinical practice.
CEO & Co-Founder, Harrison.ai
Global impact.
Annalise.ai granted Medicare New Technology Add-on Payment (NTAP)
Harrison.ai Welcomes Robyn Denholm As Board Director
Sunway Medical Centre Teams Up with Annalise.ai to Improve Patient Care in Malaysia
Annalise.ai launches the most comprehensive radiology triage suite for chest X-rays and head CTs in the U.S
Mass General Brigham and Annalise.ai collaborate to deploy, develop and research AI imaging tools
Transformational AI diagnostic tool made available to radiologists in over 40 NHS trusts
Annalise.ai Technology Features in UK TV Segment – “Lungs Matter”
Bradford Teaching Hospitals welcomes Artificial Intelligence (AI) technology innovation in Radiology to improve patient care
Annalise Enterprise CXR granted CE Mark under MDR class IIb for clinical use in EU
Annalise CXR is CE marked for use in the UK and Europe
Annalise.ai announces FDA 510(k) clearance for additional CXR findings
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