EIBIR 7 - Artificial intelligence (AI) for health imaging: pioneering cancer image repositories for diagnosis and analysis

1. To learn how to build pan-cancer imaging and multi-omics data registries in a public-private collaborative environment.
2. To understand how to overcome challenges related to image quality heterogeneity across European sites.
3. To appreciate the main challenges to be solved by the AI community in different types of solid tumours.

1. To learn about European infrastructures for large-scale biomedical data.
2. To appreciate how these infrastructures can be leveraged to build secure cancer imaging repositories.
3. To understand how these cancer imaging repositories can enhance AI in cancer imaging.

1. To elaborate on benefits and challenges related to decentralised data storage.
2. To elaborate on how health data sharing empowers AI-supported tools for cancer diagnosis and prediction.
3. To highlight the role of healthcare professionals in the development of AI tools.

1. To elaborate on challenges in a multicentric setup.
2. To present initial results focusing on AI-powered prostate cancer detection and characterisation.
3. To discuss safe translation to the clinics.

1. To learn how a pan-European digital federated infrastructure of cancer-related images and data can be used for the development of AI tools toward precision medicine.
2. To appreciate the seamless access to de-identified, high-quality real-world data, to foster collaboration among clinicians, researchers, and innovators.
3. To understand how AI data-driven decisions can be designed in diagnosis and treatment.