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

A study reveals that 74% of healthcare AI research papers still use private datasets or don't share code, creating reproducibility issues that undermine trust in medical AI applications. Papers that embrace open practices by sharing both public datasets and code receive 110% more citations on average, demonstrating clear benefits for scientific impact.

• →74% of AI healthcare papers rely on private datasets or don't share code, hampering reproducibility.
• →Papers using public datasets and shared code receive 110% more citations than those that don't.
• →Inconsistent data preprocessing pipelines lead to variable model performance reports even for identical tasks.
• →Open science practices are essential for building trustworthy AI systems in healthcare settings.
• →The AI healthcare community needs standardized guidelines and robust benchmarks to improve reproducibility.