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Deep learning models for arrhythmia detection require large, balanced datasets to achieve clinically acceptable performance. Rare arrhythmias such as ventricular tachycardia, ventricular fibrillation, and complete heart block are severely under-represented in public ECG repositories, leading to classifiers that perform well on normal sinus rhythm but fail catastrophically on minority classes. Traditional data augmentation techniques including scaling, noise addition, and time warping cannot generate new arrhythmia morphological patterns. Real-world collection of rare arrhythmia events is impractical due to low prevalence, ethical constraints, and the need for expert annotation. We present a diffusion-based generative framework that synthesizes realistic ECG signals with controlled arrhythmia patterns. The architecture comprises a conditional denoising diffusion probabilistic model trained on a small set of labeled arrhythmia examples, enabling unlimited generation of specific arrhythmia types including atrial fibrillation, ventricular tachycardia, and premature ventricular contractions. The framework includes three core components: (1) an ECG diffusion model with a 1D U-Net denoising architecture, (2) a condition encoder that accepts arrhythmia class labels and optional morphological parameters, and (3) a downstream classifier training pipeline that leverages synthetic data to correct class imbalance. This approach generates unlimited realistic arrhythmia examples with preserved morphological features including QRS duration, QT interval, and RR interval dynamics. The generative process inherently resists membership inference attacks, providing a privacy-preserving alternative to sharing real patient ECGs. The proposed framework offers a viable pathway toward balanced, privacy-preserving ECG datasets for arrhythmia detection, requiring only a small seed set of labeled rare arrhythmia examples to generate clinically useful synthetic data.

Generative artificial intelligence (AI), including GANs, VAEs, and diffusion models, is increasingly used for synthesizing and enhancing medical images, helping address challenges such as limited data, expensive acquisition, and rare disease representation. This systematic review examines studies on generative AI methods for MRI, CT, X-ray, and pathology image synthesis from 2017 to 2026, focusing on synthesis tasks, evaluation strategies, and clinical utility. A PRISMA 2020-compliant search of PubMed, IEEE Xplore, Scopus, and Web of Science identified peer-reviewed research on generative models for medical image synthesis, augmentation, harmonization, or cross-modality translation. Findings show a shift from GAN-based methods to diffusion models post-2022, with MRI and CT studies emphasizing cross-modality translation, and X-ray and pathology studies focusing on augmentation and diagnostic utility. Despite GANs' continued dominance, diffusion models are gaining traction for improving image fidelity and diversity. However, evaluation practices remain inconsistent, with limited inclusion of clinically relevant assessments. This review follows PRISMA 2020 guidelines and provides a narrative synthesis of the evidence.