Transfer Learning Models for Precision Medicine: A Review of Current Applications - Pamungkas, Yuri; Aung, Myo Min; Yulan, Gao; Uda, Muhammad Nur Afnan; Hashim, Uda (Universitas Ahmad Dahlan, 2025)
In recent years, Transfer Learning (TL) models have demonstrated significant promise in advancing precision medicine by enabling the application of machine learning techniques to medical data with limited labeled information. TL overcomes the challenge of acquiring large, labeled datasets, which is often a limitation in medical fields. By leveraging knowledge from pre-trained models, TL offers a solution to improve diagnostic accuracy and decision-making processes in various healthcare domains, including medical imaging, disease classification, and genomics. The research contribution of this review is to systematically examine the current applications of TL models in precision medicine, providing insights into how these models have been successfully implemented to improve patient outcomes across different medical specialties. In this review, studies sourced from the Scopus database, all published in 2024 and selected for their