Imagine a smart assistant for heart doctors that doesn't just guess, but explains exactly why a patient is at risk. It looks at a person's unique health history to predict if they might end up in the hospital or need a change in medicine. Because it follows strict rules for fairness and honesty, doctors and patients can actually trust the suggestions it makes.
AI in cardiology is often rejected by doctors and patients because it lacks transparency and trust. This prevents the shift toward personalized care that could reduce heart failure mortality.
A common data model for cardiovascular data and a set of 122 clinical and regulatory requirements for trustworthy AI.
If you are a medical software developer dealing with low clinician adoption of AI tools — this project developed a standardized methodological system for ethical AI that increases trust and acceptance among cardiologists.
If you are a drug development firm dealing with the need for personalized medicine — this project developed AI solutions that dissect precise patient characteristics to tailor medication and interventions to specific risk profiles.
If you are a private cardiology clinic dealing with unpredictable patient trajectories and hospitalizations — this project developed a risk assessment tool validated in 8 clinical centres to predict early cardiac events.
Based on available project data, no pricing or cost information is provided as this is a Horizon-RIA research project.
The project tests transferability across 8 clinical centres in various income levels, suggesting a design intended for broad scale and diverse environments.
Based on available project data, specific licensing terms are not listed, though it involves a consortium of 16 partners including 3 SMEs.
Yes, the project specifically focuses on AI regulation and uses the FUTURE-AI guidelines to ensure the AI is ethical and deployable in healthcare.
The project runs from 2023-06-01 to 2027-05-31, indicating it is currently in the development and validation phase.
The consortium is diverse, consisting of 16 partners across 12 countries. It maintains a balanced mix of 6 research organizations, 5 universities, and 3 industry partners (all of which are SMEs), resulting in an industry ratio of 19%. This structure suggests a strong academic foundation with a targeted path toward SME-led commercialization.
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Contact us to explore licensing opportunities for the AI4HF data model.
