The modernisation of newborn screening as a pan-European challenge - An international delphi study.

Newborn screening is a public health measure to diagnose rare diseases at birth, thereby minimising negative effects of late treatment. Genomic technologies promise an unprecedented expansion of screened diseases at low cost and with transformative potential for newborn screening programmes. However, barriers to the public funding of genomic newborn screening are poorly understood, particularly in light of the heterogenous European newborn screening landscape.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening.

Background: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem.

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study.

Background: The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application.

Challenges in mapping European rare disease databases, relevant for ML-based screening technologies in terms of organizational, FAIR and legal principles: scoping review.

Background: Given the increased availability of data sources such as hospital information systems, electronic health records, and health-related registries, a novel approach is required to develop artificial intelligence-based decision support that can assist clinicians in their diagnostic decision-making and shorten rare disease patients' diagnostic odyssey. The aim is to identify key challenges in the process of mapping European rare disease databases, relevant to ML-based screening technologies in terms of organizational, FAIR and legal principles.

Methods: A scoping review was conducted based on the PRISMA-ScR checklist.