Bridging the Reproducibility Divide: Open Source Software's Role in Standardizing Healthcare AI

2026-03-02

AI Summary

This study addresses the persistent challenge of irreproducibility in medical AI research, often stemming from reliance on proprietary data and absent code, which undermines scientific credibility and clinical translation. Through a combination of bibliometric analysis, reproducibility audits, and evaluation of open-source practices, this work quantifies—for the first time—that papers releasing both data and code receive, on average, 110% more citations. Building on these findings, the study proposes a novel pathway centered on open-source software to standardize preprocessing pipelines and establish robust benchmarks. This approach offers empirical evidence and a practical framework for developing medical AI systems that are safe, effective, and trustworthy, thereby advancing both scientific rigor and real-world applicability in healthcare AI.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Our analysis of recent AI4H publications reveals that, despite a trend toward utilizing open datasets and sharing modeling code, 74% of AI4H papers still rely on private datasets or do not share their code. This is especially concerning in healthcare applications, where trust is essential. Furthermore, inconsistent and poorly documented data preprocessing pipelines result in variable model performance reports, even for identical tasks and datasets, making it challenging to evaluate the true effectiveness of AI models. Despite the challenges posed by the reproducibility crisis, addressing these issues through open practices offers substantial benefits. For instance, while the reproducibility mandate adds extra effort to research and publication, it significantly enhances the impact of the work. Our analysis shows that papers that used both public datasets and shared code received, on average, 110% more citations than those that do neither--more than doubling the citation count. Given the clear benefits of enhancing reproducibility, it is imperative for the AI4H community to take concrete steps to overcome existing barriers. The community should promote open science practices, establish standardized guidelines for data preprocessing, and develop robust benchmarks. Tackling these challenges through open-source development can improve reproducibility, which is essential for ensuring that AI models are safe, effective, and beneficial for patient care. This approach will help build more trustworthy AI systems that can be integrated into healthcare settings, ultimately contributing to better patient outcomes and advancing the field of medicine.

Problem

• reproducibility
• healthcare AI
• open source
• data preprocessing
• model evaluation

Innovation

• reproducibility
• open-source software
• AI in healthcare
• standardization
• data preprocessing