Data Integrity in Medical AI

The reliability and ethical use of artificial intelligence (AI) in medicine fundamentally depend on the integrity of underlying data. This peer-review-style report examines data integrity - covering data quality, noise, bias, and data collection design - as the foundation of trustworthy medical AI. Medical datasets are typically small, heterogeneous, high-dimensional, and shaped by human and technical variability, making them particularly vulnerable to quality deficits. We show how poor data quality, excessive noise, and systematic bias distort analyses and clinical decisions. We outline a ten-step framework for robust data collection that spans clear study objectives, population definition, standardized protocols, noise and bias mitigation, ethical oversight, and quality control. Beyond conceptual discussion, the report integrates practical examples, methodological considerations, and ethical implications to highlight data integrity as both a scientific and moral requirement for transparent, equitable, and clinically reliable AI. Ultimately, only well-curated, representative, and unbiased data allow medical AI to support safe patient care and advance precision medicine.