Ethical Implications of e-Health Applications in Early Preventive Healthcare

As a means of preventive medicine early detection and prevention examinations can identify and treat possible health disorders or abnormalities from an early age onwards. However, pediatric examinations are often widely spaced, and thus only snapshots of the children's and adolescents' developments are obtained. With e-health applications parents and adolescents could record developmental parameters much more frequently and regularly and transmit data directly for ongoing evaluation. AI technologies could be used to search for new and previously unknown patterns. Although e-health applications could improve preventive healthcare, there are serious concerns about the unlimited use of big data in medicine. Such concerns range from general skepticism about big data in medicine to specific challenges and risks in certain medical areas. In this paper, we will focus on preventive health care in pediatrics and explore ethical implications of e-health applications. Specifically, we will address opportunities and risks of app-based data collection and AI-based data evaluation for complementing established early detection and prevention examinations. To this end, we will explore the principle of the best interest of the child. Furthermore, we shall argue that difficult trade-offs need to be made between group benefit on the one hand and individual autonomy and privacy on the other.

Automated summary

Early detection and prevention examinations in pediatrics can identify and treat health disorders from an early age onwards. However, these examinations are often widely spaced, leading to limited information. E-health applications and AI technologies can enable more frequent and regular recording of developmental parameters and search for new patterns. Despite the potential benefits, concerns exist about the unlimited use of big data in medicine, especially in pediatrics. This paper explores the ethical implications of e-health applications and discusses the opportunities and risks of app-based data collection and AI-based data evaluation in complementing established examinations.