Work

Evaluating the Utility of Synthetic Data in Medical Machine Learning Using Pairwise Correlation Distance

Year: 2025

Type: conference-paper

Abstract: Introduction: The development of predictive models for medical applications is often hindered by the scarcity of high-quality datasets, particularly for rare diseases with limited documented cases. Incorporating synthetic data (SD) generated by advanced generative models (GMs) presents a promising solution [for full text, please go to the a.m. URL]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25gmds093

Authors: Gamisch, John, Sadeghi, Sina, Kirsten, Toralf

Institutions: University Hospital Leipzig, Leipzig University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7103896011

FWCI:

Cites:

Cited by:

Related to: