Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

Background Chronic inflammatory skin diseases such as atopic dermatitis (AD) and psoriasis (PSO) present major challenges in health care. Thus, biomarkers to identify disease trajectories and response to treatments to improve the lives of affected individuals warrant great research consideration. The requirements that these biomarkers must fulfil for use as practical clinical tools have not yet been adequately investigated. Aim To identify the core elements of high-quality AD and PSO biomarkers to prepare recommendations for current biomarker research. Method A cross-sectional two-round Delphi survey was conducted from August to October 2019 and October to November 2020. All participants were members of the BIOMAP project, an EU-funded consortium of clinicians, researchers, patient organizations and pharmaceutical industry partners. The first round consisted of three open-ended questions. Responses were qualitatively analysed, and 26 closed statements were developed. For the second round, 'agreement' was assumed when the responses of ≥70% of the participants were ≥5 points on a 7-point Likert scale for each statement. Priority classification was based on mean scores (<20th percentile = low, 20th to 60th percentile = medium, >60th percentile = high). Results Twenty-one and twenty-six individuals participated in rounds one and two, respectively. From 26 statements that were included in round 2, 18 achieved agreement (8 concerning the performance, 8 for the purpose and 2 on current obstacles). Seven statements were classified as high priority, e.g. those concerning reliability, clinical validity, a high positive predictive value, prediction of the therapeutic response and disease progression. Another seven statements were assigned medium priority, e.g. those about analytical validity, prediction of comorbidities and therapeutic algorithm. Low priority included four statements, like those concerning cost effectiveness and prediction of disease flares. Conclusion The core requirements that experts agreed on being essential for high-quality AD and PSO biomarkers require rapid validation. Biomarkers can therefore be assessed based on these prioritized requirements.

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 1 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.