Synthetic data boosts medical foundation models

IF 26.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Nature Biomedical Engineering Pub Date : 2025-04-08 DOI:10.1038/s41551-025-01375-y

Bin Sheng, Pearse A. Keane, Yih-Chung Tham, Tien Yin Wong

引用次数: 0

Abstract

Using synthetic data generated via conditioning with disease labels can enhance the pretraining efficiency and generalization of medical foundation models, as shown for the detection of eye diseases via fundus photographs.

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来源期刊

Nature Biomedical Engineering Medicine-Medicine (miscellaneous)

CiteScore

45.30

自引率

1.10%

发文量

138

期刊介绍： Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.