Synthetic data in machine learning for medicine and healthcare
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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.
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SHEPHERD's model can be publicly released without the risk of exposing patient information.
“A notable byproduct of training the model on synthetic data is that SHEPHERD’s model can be publicly released without the risk of exposing patient information52.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [52]
The Fidelity tests section says synthetic datasets could be freely shared and would substantially lessen patient-privacy concerns. It does not establish that SHEPHERD’s model can be released without risk; the same section warns that publicly available model weights can leak patient information. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSynthetic data generators were applied for building similar replicas of original private datasets while maintaining consistent statistical properties.
“Additionally, the synthetic data generators were applied for building similar replicas of the original private datasets, while maintaining consistent statistical properties [161,162].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [162]
The Fidelity tests section says synthetic datasets that accurately capture the original data distribution could be shared instead of patient data, supporting the idea of statistically similar substitutes for private datasets. But it presents this as a possibility, not an established outcome of applying generators, and cautions that current generative models are not ready for off-the-shelf use. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDeep generative models are used to create synthetic images such as skin lesions, pathology slides, colon mucosa, and chest x-rays, thereby greatly improving the reproducibility of medical data.
“For instance, deep generative models are used to create synthetic images such as skin lesions, pathology slides, colon mucosa, and chest x-rays, thereby greatly improving the reproducibility of medical data [71].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [71]
The Fidelity tests section lists synthetic images of skin lesions, pathology slides, colon mucosa and chest X-rays. It says synthetic data could contribute to improving reproducibility, but does not show that generating these images has greatly improved it; the section also cautions that current generative models are not ready for off-the-shelf use. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPretraining with synthetic fine-grained morphological descriptions has utility, suggesting the scaling potential of TITAN pretraining with synthetic data.
“Additionally, we demonstrate the utility of pretraining with synthetic fine-grained morphological descriptions, suggesting the scaling potential of TITAN pretraining with synthetic data89–91.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [89]
Research has used synthetic data in AI for medicine and health care to address the scarcity of annotated medical data in the real world.
“By contrast, research has used synthetic data in AI for medicine and health care to address the scarcity of annotated medical data in the real world [71].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [71]
Strategies such as adopting synthetic data for training models provide new opportunities to improve the diversity and robustness of ML and AI models by reducing patient privacy concerns and facilitating data sharing while maintaining the original distribution of data.
“Strategies such as adopting synthetic data for training models provide new opportunities to improve the diversity and robustness of ML and AI models by reducing patient privacy concerns and facilitating data sharing while maintaining the original distribution of data [63].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [63]
This unethical use of AI is becoming increasingly likely since generative models can be used to create synthetic datasets from scratch or make alternative versions of existing datasets.
“This unethical use of AI is becoming increasingly likely since generative models can be used to create synthetic datasets from scratch or make alternative versions of existing datasets [50, 155, 200, 214].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [50]
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A multimodal whole-slide foundation model for pathology
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“The largest gap for 1-shot is observed in the OT108 task, where TITAN outperforms ABMIL by 56.7%, with similar trends in prototyping evaluation (Supplementary Tables 86–89).”
InfluentialSelf-citationGenerative models improve fairness of medical classifiers under distribution shifts
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- Authors
- Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Mahmood, Faisal
- Venue
- Nature Biomedical Engineering
- Published
- 2021-06-15
- Volume
- 5
- Issue
- 6
- Pages
- 493-497
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC9353344
- OpenAlex
- W3166254754
- Semantic Scholar
- aed2da5f3612627f7a650a4f13b9be66d49e33da
- Cited by
- 728
- References
- 54
- Citations by year
- 2026: 126 · 2025: 175 · 2024: 185 · 2023: 152 · 2022: 82 · 2021: 8
- FWCI
- 30.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM138216)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- synthetic data, machine learning in medicine, health care, artificial intelligence, software vulnerabilities, data privacy, AI policy
- MeSH terms
- Machine Learning, Carcinoma, Renal Cell, Delivery of Health Care, Diagnosis, Computer-Assisted, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Information Theory, Kidney Neoplasms, Quality Control, Synthetic Biology, Datasets as Topic
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