Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI

Regulatory and operational pressures significantly shape medical AI datasets before annotation even begins. The paper identifies five key factors that constrain dataset creation, including regulatory requirements, commercial pressures, and operational limitations—meaning the quality of medical AI training data is determined by forces beyond just the annotation process itself.

"Ground truth schema design" (how we define what we're labeling) is a critical but overlooked step in responsible AI. Rather than focusing solely on annotation quality, the researchers emphasize that decisions about what to label and how to define it are foundational choices that deserve more attention from AI developers and ethicists.

Disagreements among medical experts create inherent limits to labeling accuracy. The paper identifies "epistemic differences" as an internal factor—meaning that even domain experts may legitimately disagree on correct answers, making perfect ground truth impossible in medical contexts and requiring developers to acknowledge this uncertainty.

The context where data is collected and used matters as much as the data itself. External factors like where and why data is gathered shape what datasets can realistically be created, meaning responsible AI requires understanding these contextual constraints rather than treating datasets as neutral technical artifacts.

High-quality medical AI datasets require acknowledging what's not possible to create. The paper argues that responsible AI discussions must account for real-world constraints and limitations, rather than pursuing idealized datasets that may be impractical or inappropriate given regulatory, commercial, and epistemic realities.