Reimagining medical AI with the most powerful large multimodal foundational model designed to excel in radiology

Introducing Harrison.rad.1, the most capable radiology foundational model today*

Authors: Dr Suneeta Mall, Head of AI Engineering, Harrison.ai, Dr Jarrel Seah, Director of Clinical AI, Harrison.ai

Advancing what is possible in healthcare through AI

Global healthcare is currently facing several challenges, such as increasing imaging volumes, a growing number of images that radiologists must review per case, a shortage of medical professionals, and a high psychological burden on the existing staff [1]. Foundational models or multimodal large language models (LLMs) have potential benefits that could mitigate these challenges.

Multimodal LLMs advance the scope and capabilities of artificial intelligence and deep learning. And yet, even as they have made inroads into everyday life since the introduction of OpenAI’s ChatGPT and similar models, their adoption in a critical and highly regulated space like healthcare still remains limited.

Current regulatory frameworks for medical devices are restricted to proprietary, specific AI models that are only capable of pre-defined tasks and intended use cases.

Both regulatory frameworks and their approved medical device AI models do not support continuous learning or generalisation to areas in which the model has not been trained. The limitations are not without reason: they are designed to ensure quality and safety, while mitigating and preventing risks for individuals and societies, including potential misuse.

In contrast, multimodal LLMs can be used in ways that transcend their original application, increasing their potential to impact and scale global healthcare.

They are open-domain models, capable of tasks that may not have been specified during training—similar to the way clinicians may draw conclusions based on their knowledge and experience when they encounter new conditions.

Evaluating such models for use in radiology presents new challenges. We need to move to a paradigm where we test them not only on their abilities to recognise individual pathologies but also on their radiology interpretation skills in general.

Harrison.rad.1 – designed to excel in radiology tasks

Harrison.rad.1 is a radiology-specific multimodal LLM by Harrison.ai that has been trained to excel in radiology tasks. It is a dialogue-based model, which accepts interleaved text and visual inputs and generates both structured and unstructured text outputs. Factual correctness and clinical accuracy are the model’s key priorities.

Unlike general-purpose generative AI models such as the GPT or Gemini family models, Harrison.rad.1 has been trained on millions of DICOM images from radiology studies and radiology reports across all X-ray modalities and trained to reason over radiology images and text. This extensive training on real-world, diverse and anonymised patient data enables Harrison.rad.1 to excel at radiology tasks.