Exploring the role of synthetic data in the future of AI in healthcare: A scoping review of frameworks, challenges, and implications

Abstract

Synthetic data has emerged as a transformative tool in healthcare, particularly in areas such as medical imaging, electronic health records (EHRs), and clinical trial simulation, where data privacy, diversity, and accessibility are critical. This scoping review examines current approaches to synthetic data generation in healthcare, with a focus on AI model training, privacy preservation, and bias mitigation. A comprehensive search of PubMed, IEEE Xplore, and ACM Digital Library yielded 2,906 studies, of which 42 met the inclusion criteria. Key data generation techniques included generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, Bayesian networks, federated learning, recurrent neural networks (RNNs), large language models (LLMs), agent-based models, graph-based generators, and SMOTE-based oversampling. Applications ranged from diagnostic model development to privacy-preserving data sharing and educational simulation. However, the field faces persistent challenges, including inconsistent validation practices, the absence of standard benchmarks, high computational demands, and ethical concerns related to consent and bias. This review underscores the need for standardized evaluation protocols, clearer regulatory guidance, and multidisciplinary collaboration to ensure the safe, equitable, and effective use of synthetic data in healthcare AI. In addition to technical advances, the review highlights the socio-technical implications of synthetic data adoption, including its impact on health equity, patient trust, and clinical decision-making. • This PRISMA-ScR scoping review screens 2,906 records and synthesizes 42 studies (2017–2024), providing a comprehensive, transparent evidence map. • It unifies a fragmented field by presenting a clear taxonomy of generators (GANs, VAEs, diffusion, LLMs, federated, graphs, Bayesian nets, RNNs, ABMs) and application domains (EHR, imaging, operations). • It translates the literature into actionable evaluation guidance that links fidelity, task utility, and privacy risk to improve reporting and comparability. • It bridges technical and ethical perspectives, converting risks (bias, consent, ownership, re-identification) into concrete governance and practice recommendations. • It sets a forward research and implementation agenda that institutions can apply immediately to deploy synthetic data safely, fairly, and on a scale.

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