  Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but
when used on new populations, model generalizability issues limit their
efficacy. Generative AI, particularly denoising diffusion probabilistic models
(DDPMs), offers a promising approach to generating synthetic images, enhancing
dataset diversity. This study investigates the impact of synthetic data
supplementation on the performance and generalizability of medical imaging
research. The study employed DDPMs to create synthetic CXRs conditioned on
demographic and pathological characteristics from the CheXpert dataset. These
synthetic images were used to supplement training datasets for pathology
classifiers, with the aim of improving their performance. The evaluation
involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and
various experiments, including supplementing real data with synthetic data,
training with purely synthetic data, and mixing synthetic data with external
datasets. Performance was assessed using the area under the receiver operating
curve (AUROC). Adding synthetic data to real datasets resulted in a notable
increase in AUROC values (up to 0.02 in internal and external test sets with
1000% supplementation, p-value less than 0.01 in all instances). When
classifiers were trained exclusively on synthetic data, they achieved
performance levels comparable to those trained on real data with 200%-300% data
supplementation. The combination of real and synthetic data from different
sources demonstrated enhanced model generalizability, increasing model AUROC
from 0.76 to 0.80 on the internal test set (p-value less than 0.01). In
conclusion, synthetic data supplementation significantly improves the
performance and generalizability of pathology classifiers in medical imaging.
