# Reproducibility in Machine Learning for Health

arXiv: https://arxiv.org/abs/1907.01463 · HTML: https://mlsanity.com/paper/1907.01463/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 25 of 25)

- [Randomness In Neural Network Training: Characterizing The Impact of Tooling](https://mlsanity.com/paper/2106.11872/) (2021)
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- [How I failed machine learning in medical imaging -- shortcomings and recommendations](https://mlsanity.com/paper/2103.10292/) (2021)
- [Reproducibility in NLP: What Have We Learned from the Checklist?](https://mlsanity.com/paper/2306.09562/) (2023)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Assessing SATNet's Ability to Solve the Symbol Grounding Problem](https://mlsanity.com/paper/2312.11522/) (2023)
- [MRxaI: Black-Box Explainability for Image Classifiers in a Medical Setting](https://mlsanity.com/paper/2311.14471/) (2023)
- [Reproducibility in machine learning for medical imaging](https://mlsanity.com/paper/2209.05097/) (2022)
- [The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks](https://mlsanity.com/paper/2506.08274/) (2025)
- [Disease Progression Modeling Workbench 360](https://mlsanity.com/paper/2106.13265/) (2021)
- [Artificial Intelligence in Healthcare: Lost In Translation?](https://mlsanity.com/paper/2107.13454/) (2021)
- [Several fitness functions and entanglement gates in quantum kernel generation](https://mlsanity.com/paper/2309.03307/) (2023)
- [Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standards](https://mlsanity.com/paper/2510.18238/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [Confidence Intervals for Performance Estimates in Brain MRI Segmentation](https://mlsanity.com/paper/2307.10926/) (2023)
- [ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning](https://mlsanity.com/paper/2503.13570/) (2025)
- [Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions](https://mlsanity.com/paper/2507.14077/) (2025)
- [Survey of Trustworthy AI: A Meta Decision of AI](https://mlsanity.com/paper/2306.00380/) (2023)
- [Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction](https://mlsanity.com/paper/2601.16406/) (2026)
- [Bridging the Reproducibility Divide: Open Source Software's Role in Standardizing Healthcare AI](https://mlsanity.com/paper/2603.03367/) (2026)
- [AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models](https://mlsanity.com/paper/2605.17765/) (2026)
- [Radiuma: A Unified Zero-Code Executable Graphical Workflow Generator for Reproducible and Shareable Medical Image Analysis and Machine Learning](https://mlsanity.com/paper/2605.24201/) (2026)
- [Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification](https://mlsanity.com/paper/2607.25232/) (2026)
- [Rethinking How We Evaluate Methodological Progress in Health AI](https://mlsanity.com/paper/2609.18134/) (2026)
- [When Participant Disjoint Validation Is Not Enough to Establish Generalization in Parkinson's Gait Classification](https://mlsanity.com/paper/2610.16220/) (2026)

## Abstract (arXiv)

Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data and code accessibility. Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.