Short abstract

Synthetic medical data is touted as privacy‑preserving but is not value‑neutral. Prioritizing privacy over transparency and scalability over fidelity erodes explainability, trust, and patient‑centered care, producing “zombie data,” more reification, and fungibility that reshape the medical realm.

Long abstract

Synthetic medical data (SMD) has been introduced to address data-acquisition challenges. It has been described as a “magical” solution that enables model training without compromising patients’ privacy and is therefore considered ethically desirable (Bellovin et al. 2019; Savage, 2023). However, as we will show in this article, synthetic data is not value-neutral, and it poses both epistemic and ethical trade-offs that cumulatively affect the medical realm. We focus on three primary methodologies: two key deep learning approaches, namely generative models without privacy guarantees and differential privacy (DP) models. In addition, we will use vine copula models as our primary example to demonstrate the statistical approach. We show that prioritizing privacy over transparency undermines explainability, which in turn hinders trust and fairness, pushing these values down the ladder. We also show that prioritizing scalability over fidelity moves medicine away from personalized, participatory, and patient-centered approaches and toward a naturalistic, mechanistic understanding. The collapse of context and experiential knowledge into “zombie data” yields a narrow conception of utility in medicine that ignores the patient as a person. The absence of a direct real-world reference (combined with the use of this data to train algorithms) is where SMD makes a step forward in the reification process relative to “regular” medical data. The data collected to represent reality may gradually subsume the very thing it is intended to represent. As such, SMD also introduces a new degree of reification in medicine, promoting fungibility, which offers clear advantages for data-intensive enterprises, over patients and minorities.