TY - JOUR
T1 - Generative Artificial Intelligence for Synthetic Electronic Health Record Data Generation: A Critical Review of Methods, Privacy Risks, Fidelity Metrics, and Downstream Task Utility
AU - Victor Hugo
AU - Daniel Cruz
AU - Javier Salazar
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2026
VL - 5
IS - 1
SP - 124
N2 - Synthetic electronic health record (EHR) data generation has emerged as a potential solution to balancing clinical data accessibility with patient privacy, using generative artificial intelligence to simulate tabular, longitudinal, and textual health records without exposing identifiable patient information. This critical review, informed by PRISMA-ScR methodology, examines studies published between 2017 and 2025 focusing on generative models for synthetic EHR creation, with particular attention to privacy risks, data fidelity, downstream task utility, and ethical or regulatory considerations. A total of 67 studies were included after systematic screening, showing a dominance of GAN-based approaches alongside growing use of diffusion models and large language models in recent years, although privacy assessment and benchmarking practices remain inconsistent. Overall, the evidence suggests that while synthetic EHR data can facilitate data sharing, research, and model development, achieving a balance between realism, utility, and privacy remains challenging, as high statistical fidelity does not necessarily translate into clinical usefulness and strong downstream performance does not ensure adequate privacy protection.
UR - https://cirpublications.com/v069048730
ER -