Synthetic Medical Data vs. The Four Principles: Ethical Trade-Offs and Zombie Data

Rapid advances in AI for medical applications are accompanied by an increasing demand for vast datasets. Privacy constraints - legal restrictions such as GDPR, as well as more general ethical concerns - pose significant barriers to the acquisition and use of data. Synthetic medical data (SMD) is AI-generated data that mimics real patient information. SMD is being promoted as an ethical solution that preserves privacy while enabling scalable model training. However, as we will show, the use of SMD raises its own set of issues. SMD's emphasis on privacy and scalability subtly embeds ethical and epistemic trade-offs that undermine the principles commonly regarded as being paramount in medical ethics. These trade-offs lack the kind of ethical justification one would expect in decisions that disrupt commonly accepted values and priorities, such as those captured by Beauchamp and Childress' four principles. In the discourse on the ethical advantages of SMD, privacy tends to be treated as a value in its own right. We show that this is a problematic assumption. Transparency, privacy, scalability, and fidelity are all linked in complex ways with the principles of medical ethics. We map these relationships and show that when privacy is privileged over other values, it conflicts with autonomy and beneficence. Our aim here is not to establish that this privileging is wrong per se, but to show that it needs careful analysis before we can accept the idea that SMD is indeed ethically advantageous.