Synthetic data in machine learning for medicine and healthcare

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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Bibliographische Detailangaben
Veröffentlicht in:	Nature biomedical engineering 2021-06, Vol.5 (6), p.493-497
Hauptverfasser:	Chen, Richard J., Lu, Ming Y., Chen, Tiffany Y., Williamson, Drew F. K., Mahmood, Faisal
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 631/114/1564 692/308/575 692/700/1538 Artificial intelligence Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Carcinoma, Renal Cell - diagnostic imaging Carcinoma, Renal Cell - pathology Comment Datasets as Topic Delivery of Health Care - organization & administration Diagnosis, Computer-Assisted - methods Diagnostic Imaging Health care Humans Image Interpretation, Computer-Assisted - statistics & numerical data Information Theory Kidney Neoplasms - diagnostic imaging Kidney Neoplasms - pathology Learning algorithms Machine learning Machine Learning - standards Machine Learning - supply & distribution Medicine Quality Control Synthetic Biology - instrumentation Synthetic Biology - methods Synthetic data
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Beschreibung
Zusammenfassung:	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.
ISSN:	2157-846X 2157-846X
DOI:	10.1038/s41551-021-00751-8