238 ERC projects using AI in health
A new ‘Feedback for Policy’ report analyzes 238 ERC projects leveraging AI in the health sector, funded under FP7, Horizon 2020 and Horizon Europe, with a total value of €450 million. These projects leverage AI for disease prevention and early detection, diagnosis, treatment optimization, and long-term disease management, and have developed AI models, including machine learning and deep learning, and clinical decision support systems and platforms.
This study shows how AI-based models, clinical decision support systems and platforms, including machine learning and deep learning, are being developed to enable early detection of disease and more personalized risk prediction, diagnosis, prognosis and treatment. We will also highlight how AI supports the integration of multi-omics, phenotypic, and health data, contributing to the entire drug lifecycle from drug discovery to clinical trials.
Links to EU AI law and the European Health Data Space
This report shows how ERC projects can support the implementation of EU AI legislation that classifies most AI-based software intended for medical purposes as “high risk”, as well as the European Medical Data Space and the EU’s Applied AI Strategy. ERC-funded researchers emphasize the need for rigorous verification, robust risk management, high-quality data, transparency, and meaningful human oversight, in addition to secure infrastructure and clear data governance.
Gerd Gigerenzer, former vice president of the ERC Scientific Council, said:
Our analysis shows that the real impact depends not only on better algorithms, but also on how the algorithms are designed, validated, and managed. Smart technology requires smart institutions. Without high-quality data, transparent models, meaningful human oversight, and clear accountability, we will not be able to realize the full potential of AI in healthcare.
Case studies, enablers and next steps
A closer look at the 59 projects and 20 case studies shows applications in disease detection and monitoring, drug discovery, risk prediction, imaging, medical robotics, and personalized medicine, pointing to long-term funding, AI hubs for science, and regulatory sandboxes as key enablers.
This report shows how frontier research can help ensure that AI in healthcare is not only innovative and competitive, but also reliable, human-centered, and solidly evidence-based.
