Edition of 30 November 2023

A new article has been published in The Lancet Regional Health – Europe which analyses newborn screening (NBS) policy in the United Kingdom by comparing current practice to the set of principles developed by EURORDIS.

EURORDIS developed their NBS principles in order to promote a harmonised and uniform approach to NBS across Europe. These patient-centred recommendations focus on best practice for policy-making, and national-level implementation of screening programs. In particular, the EURORDIS principles highlight the need to screen for “actionable” conditions (i.e., conditions for which early intervention has a significant benefit for patients and/or their families); the importance of evidence-based policy-making; the role of stakeholder engagement; considerations for data governance; and the importance of psychological, social, and economic support for families who receive a diagnosis through NBS.

Overall, the authors found that UK NBS practice is generally aligned with the EURORDIS principles. However, some controversy and challenges exist, particularly concerning the interpretation of what constitutes an “actionable” condition. Additionally, there is room for improvement when it comes to engaging patient and public voices in the development, implementation and evaluation of NBS processes.

A new article has been published in Cureus which explores how artificial intelligence (AI) and machine learning (ML) can be used to optimise diagnosis and treatment plans for rare genetic disorders. The authors discuss current applications of AI and ML in the field, as well as ethical, legal, technical, and human considerations.

AI has shown to be a powerful diagnostic tool, with ML algorithms capable of detecting genetic and phenotypic indicators of rare diseases even when trained on limited datasets. Similarly, AI can be used to expedite drug development, and to develop personalised treatment plans which optimise health outcomes and prognosis.

Despite these benefits, there are a number of challenges and limitations facing the application of AI in rare diseases. As it is still an emerging field, there are ethical considerations related to privacy, data ownership, and equity, and social attitudes towards AI-based tools vary. The development of ML algorithms also has technical demands which can be difficult to meet, particularly in contexts where resources are limited. In response to these challenges, the authors highlight the importance of measures such as transparency, regulation, and thorough evaluation of AI methods.

Looking towards the future, the article points to the emergence of patient-centred AI tools which draw on patient responses, electronic data, and patient-reported outcomes to enhance disease monitoring. It also discusses the potential of AI to be integrated into telemedicine, thereby facilitating more comprehensive virtual consultations.