# Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis

arXiv: https://arxiv.org/abs/2407.11034 · HTML: https://mlsanity.com/paper/2407.11034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 96 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Universal Source-Free Domain Adaptation](https://mlsanity.com/paper/2004.04393/) (2020)
- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Differential Privacy-enabled Federated Learning for Sensitive Health Data](https://mlsanity.com/paper/1910.02578/) (2019)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes](https://mlsanity.com/paper/1610.08853/) (2016)
- [Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features](https://mlsanity.com/paper/2208.05134/) (2022)
- [Semi-supervised Triply Robust Inductive Transfer Learning](https://mlsanity.com/paper/2209.04977/) (2022)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Federated and distributed learning applications for electronic health records and structured medical data: A scoping review](https://mlsanity.com/paper/2304.07310/) (2023)
- [Semi-supervised Transfer Learning for Evaluation of Model Classification Performance](https://mlsanity.com/paper/2208.07927/) (2022)
- [Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response](https://mlsanity.com/paper/2311.09264/) (2023)

## Built on by (most cited first, 6 of 6)

- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction](https://mlsanity.com/paper/2410.17269/) (2024)
- [SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation](https://mlsanity.com/paper/2505.08198/) (2025)
- [DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2604.22827/) (2026)
- [Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction](https://mlsanity.com/paper/2605.24212/) (2026)
- [TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk](https://mlsanity.com/paper/2512.12795/) (2025)

## Abstract (arXiv)

Clinical and biomedical research in low-resource settings often faces significant 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 pre-trained 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. In our review of TL applications in structured clinical and biomedical data, we screened 3,515 papers, with 55 meeting the inclusion criteria. Among these, only 2% (one out of 55) utilized external studies, and 7% (four out of 55) addressed scenarios involving multi-site collaborations with privacy constraints. 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.