You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly
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.
The growth of digital health data and increasing computational capabilities have created significant opportunities for the use of artificial intelligence (AI) technology in healthcare. With the ability to learn from large volumes of clinical, -omics, and other health data, AI has the potential to support a wide range of activities: diagnosis, clinical decision making, personalized medicine, clinical research, drug development, administrative processes, and the mitigation of health disparities (Shibata & Wada, 2011; Fleming, 2018; Shortliffe & Sepúlveda, 2018; Davenport & Kalakota, 2019; Fiske, Henningsen, & Buyx, 2019; Liu et al., 2019; Schork, 2019; Woo, 2019). If data-intensive medicine can realize continuous improvement of healthcare quality and thereby reduce patient harm, improve health, empower personal decision making, and increase equity, it would fulfil the core ethical principles of healthcare (ABIM Foundation, 2002; Beauchamp & Childress, 2013; McLennan et al., 2018).
The potential opportunities of AI have led many countries, particularly in the European Union (EU), to invest significant financial and human resources in AI initiatives. In the past few years, previously unseen amounts of public and private investment have flowed into AI applications (KPMG, 2018; CB Insights, 2019). National AI strategies with large, dedicated budgets were published by many EU countries (Righi et al., 2022), e.g., the German federal government promised to allocate 3 billion EUR in funding between 2020–2025 (Die Bundesregierung, 2018). Funding for healthcare and medical AI-related research projects through the EU Horizon 2020 scheme increased between 2014–2020, although large differences in investments can be seen between Member States (around 80 million EUR was awarded to projects in each of the top-funded countries and around 100.000 EUR in countries receiving the lowest amount of funding) (De Nigris et al., 2020, p. 27).
