The use of Artificial Intelligence (AI) on health data is generating promising tools for assisting clinicians in application fields such as cancer management. Increasing favourable outcomes suggests that health imaging-based AI approaches can become useful clinical tools in areas such as non-invasive tumour characterization, prediction of certain tumour features, staging of tumour spread, stratification of patients, selection of most appropriate therapies and clinical prognosis. Thus, the development and validation of health imaging AI tools is a field of great interest nowadays, not only from a research perspective but also from a legal (e.g. privacy protection and ethics), regulatory (e.g. CE marking, accountability for supported clinical decisions) and operational (e.g. standards, ontology, large repositories) perspective in the common interest of accelerating the path to market of these AI tools once they have proven to be useful, safe, legal and accepted by the clinical community.

The advancement of health imaging AI research is intrinsically linked to the availability of quality controlled large collected datasets. In the EU healthcare systems, data is collected in a comprehensive manner for each patient, all through the disease management cycle, which offers an excellent potential for the use of Big Data in cancer management. However, the access to large volume of curated datasets with controlled confounding factors remains as a major challenge. Ensuring that data are consistent and representative of the context of application (e.g. with clear established diagnosis criteria for patient inclusion) implies important trade‐offs between data quantity and data quality. The generation of quality large datasets is a resource-intensive endeavour, facing technical and operational difficulties such as data harmonisation, data curation and standardisation of annotation, as well as legal and ethical restrictions. These repositories are crucial for gathering real world evidence drawing causal inferences throughout non-interventional observational studies.

Therefore, the context of CHAIMELEON project is determined by the European approach to AI in the framework of the Digital Single Market Strategy, described in the 2018 Coordinated Plan on Artificial Intelligence, and the EU regulatory framework determined by the General Data Protection Regulation (GDPR). You can discover more about the project and its partners by viewing the project website or following its Twitter.