Table 2.

Summary of the AI4HI projects, listing their goals, use-cases, types of metadata identified so far

Project	Goal	Considered use cases	Types of metadata	Adopted models
PRIMAGE	To build an imaging biobank for the training and validation of machine learning and multiscale simulation algorithms	Paediatric neuroblastoma and diffuse intrinsic pontine glioma	DICOM tags Image analysis metadata (registration, denoising, radiomics) Clinical variables	DICOM-MIABIS OMOP CDM
EuCanImage	To build a European cancer imaging platform for enhanced AI in oncology	Eight use cases regarding liver, breast, and colorectal cancer	Imaging data Clinical variables	DICOM-MIABIS ICGC-ARGO
INCISIVE	To improve cancer diagnosis and prediction with AI and big data	Lung, breast, colorectal, and prostate cancer	Imaging data Clinical and biological data	FHIR
CHAIMELEON	To develop a structured repository of health images and related clinical and molecular data	Lung, breast, prostate, and colorectal cancer	Imaging data Clinical variables	DICOM-MIABIS OMOP CDM
ProCancer-I	To develop an AI Platform integrating imaging data and models	Prostate cancer	Imaging data Clinical variables	DICOM-Radiation therapy OMOP CDM with Oncology Extension

AI Artificial intelligence, AI4HI Artificial Intelligence for Health Imaging, DICOM Digital Imaging and Communications in Medicine, FHIR Fast Healthcare Interoperability Resources, ICGC-ARGO International Cancer Genome Consortium-Accelerating Research in Genomic Oncology, MIABIS Minimum Information About BIobank data Sharing, OMOP CDM Observational Medical Outcomes Partnership Common Data Model, SEDI Semantic DICOM