# Generalization in medical AI: a perspective on developing scalable models

arXiv: https://arxiv.org/abs/2311.05418 · HTML: https://mlsanity.com/paper/2311.05418/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks](https://mlsanity.com/paper/2303.11338/) (2023)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)

## Built on by (most cited first, 7 of 7)

- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach](https://mlsanity.com/paper/2503.14897/) (2025)
- [Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model](https://mlsanity.com/paper/2402.10965/) (2024)
- [MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events](https://mlsanity.com/paper/2506.19174/) (2025)
- [SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography](https://mlsanity.com/paper/2404.06869/) (2024)
- [uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm](https://mlsanity.com/paper/2506.11238/) (2025)
- [Collaborative Medical Triage under Uncertainty: A Multi-Agent Dynamic Matching Approach](https://mlsanity.com/paper/2507.22504/) (2025)

## Abstract (arXiv)

The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications. A three-level scale is introduced to characterize out-of-distribution generalization performance of medical AI models. This scale addresses the diversity of real-world medical scenarios as well as whether target domain data and labels are available for model recalibration. It serves as a tool to help researchers characterize their development settings and determine the best approach to tackling the challenge of out-of-distribution generalization.