Iris
AI-enabled clinical decision support platform that will accelerate course of treatment planning and monitoring, while improving accuracy of diagnostic image analysis.
Project Overview
Updated October 16, 2024
The Problem
Delivering quality clinical diagnostic patient care depends on accurate and timely analysis of diagnostic imaging and its associated data, such as patient demographic and relevant patient history information.
There is a wide array of contributing factors that pose increased risk of negatively impacting the ability to provide successful medical treatment and timely intervention, including:
- exponential increases in imaging volumes combined with growing pressure on medical imaging service providers to do more with less
- communication and workflow challenges with multiple disparate systems which decrease efficiency of collaboration and escalation/transmission of findings
- remote environments, or ER departments where an immediate diagnosis is not available, yet is essential to determine the most appropriate course-of-treatment
Additionally, with the significant backlog of patients attempting to reschedule their medical imaging procedures due to COVID-19, the growing baseline demand for medical imaging, and the capacity limitations in radiology departments, the health-care system needs solutions that can greatly optimize clinical care pathways.
How We Are Solving It
Iris is Canada’s first Artificial Intelligence-based platform to act as a co-pilot for medical diagnostic imaging analysis and clinical decision making. The platform is led by Synthesis Health, in partnership with GE Healthcare, Konica Minolta, The University of British Columbia, Vancouver Coastal Health Research Institute and BC Cancer.
Iris will increase the speed and accuracy of front-line course-of-treatment decisions, enhance the capabilities of radiologists and other physicians to consistently find abnormalities in radiology images. Additionally, it will democratize access to a modern clinical care pathway regardless of geographic or socio-economic disparities while improving the overall quality of patient care.
The Iris platform will be securely trained on 10+ years of imaging data from Canadian health authority partners. It will incorporate machine learning models, AI classifiers and interpreters that can analyze diagnostic test images in real-time to identify abnormalities, standardize measurements, determine material change, prioritize review, and better predict clinical outcomes and appropriate patient monitoring. Natural language understanding tools will be implemented to review every radiology report for quality assurance, patient monitoring, and follow-ups. The Iris platform will be built to be seamlessly integrated into the most prominent Picture Archiving and Communications Systems (PACS) and Radiology Information Systems (RIS) in Canada.
The Iris project will also establish a National Advisory Council on AI in Healthcare with key leaders from across Canada to share learnings, impact decision-making, and create a responsible balance between innovation and patient safety to enhance Canada’s reputation as a global powerhouse for AI in health care.
The Result
The Iris consortium sought to create Canada’s first AI-based platform to act as a co-pilot for medical diagnostic imaging analysis and clinical decision-making. The project included development of 9 AI solutions developed for medical imaging, including AI products for diagnostic imaging and natural language processing (NLP) market-ready solutions. It also established a technology stack that would allow for scalable, secure and rapid integration of AI into medical imaging workflows; as well as the creation of an AI education curriculum for physicians and refinement of data sharing processes and policies.
Project Lead
Project Partners
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“Our consortium is uniquely positioned to leverage scalable technology, existing machine learning development platforms, and bridge academia and industry expertise to create and integrate Iris.”
Chief Medical Officer, Synthesis Health Learn More