Synthetic data in machine learning for medicine and healthcare

By Richard J. Chen, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood. 2021

694 citations recorded by the source corpus.

The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Denoising diffusion probabilistic models for 3D medical image generation (2023, neutral): “To overcome this shortcoming of metric-based evaluations, we had human experts rate the synthetic images along three categories: (1) Image quality, (2) slice consistency, and (3) anatomic correctness.”
Diffusion models in medical imaging: A comprehensive survey (2022, background): “[45] found that using both synthetic and real data to train classifiers for histology images improves performance compared to using only real data.”
Machine Learning for Synthetic Data Generation: a Review (2023, background): “In order to protect health information and improve reproducibility in research, synthetic data has drawn mainstream attention in the healthcare industry [68], [69].”
Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “…simulate the full spectrum of clinical scenarios and disease presentations, from dangerous and rare clinical scenarios such as incorrect surgery techniques 63 , to modelling the spectrum of brain-tumour presentation 19 , to exploring the disease progression of neurodegenerative diseases 65,66 .”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, methodology): “The ability to generate realistic synthetic data offers a range of applications 108,109 .”
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy (2023, background): “The work by Chen et al. 61 sheds light on this broader perspective, underscoring the crucial role of regulatory bodies in facilitating the judicious application of synthetic data in healthcare, speci ﬁ cally within the arena of medical devices incorporating arti ﬁ cial intelligence and machine…”
Artificial intelligence for digital and computational pathology (2023, neutral): “This approach is particularly”