Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.

Abstract

Background: Clinical and biomedical research in low-resource settings often faces substantial challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training and prompt researchers to seek methods for leveraging existing knowledge from related studies to support new research efforts. Transfer learning (TL), a machine learning technique, emerges as a powerful solution by utilizing knowledge from pretrained models to enhance the performance of new models, offering promise across various healthcare domains. Despite its conceptual origins in the 1990s, the application of TL in medical research has remained limited, especially beyond image analysis. This review aims to analyze TL applications, highlight overlooked techniques, and suggest improvements for future healthcare research. Methods: Following the PRISMA-ScR guidelines, we conducted a search for published articles that employed TL with structured clinical or biomedical data by searching the SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL databases. Results: We screened 5,080 papers, with 86 meeting the inclusion criteria. Among these, only 2% (2 of 86) utilized external studies, and 5% (4 of 86) addressed scenarios involving multi-site collaborations with privacy constraints. Conclusions: To achieve actionable TL with structured medical data while addressing regional disparities, inequality, and privacy constraints in healthcare research, we advocate for the careful identification of appropriate source data and models, the selection of suitable TL frameworks, and the validation of TL models with proper baselines.