Data Due Diligence Fundamentals for Medical Imaging AI

How to reason about data before models, metrics, and validation.

Medical imaging AI projects rarely fail because of model architecture alone. More often, they fail because early data decisions were never examined with sufficient depth. Assumptions about labels, splits, representativeness, augmentation, or scope often remain implicit until they surface as unstable results or limited generalizability.

This 29-page booklet presents a foundational framework for understanding how dataset structure, labeling choices, variability, augmentation, leakage, and governance shape what a system can legitimately claim. It serves as a strategic reference to support professional judgment during the early phases of project design.

What's inside:

Nine chapters covering the full spectrum of data reasoning, including:

• Datasets as Systems: Moving beyond "images and labels" to understand structural identifiers and linkage.
• Labels as Contracts: Shifting from "ground truth" to operational definitions and handling ambiguity.
• Variability as the Default: Why representativeness is conditional and average performance hides failure modes.
• Leakage as a Design Failure: Preventing structural violations at the patient level before they inflate metrics.
• Governance & Traceability: Transforming documentation into a tool for sustaining defensible decisions.
• What Changes Once Data Due Diligence Is Taken Seriously: How taking data seriously transforms decision-making and project trajectories.

Who is this for

This booklet is intended for professionals responsible for shaping datasets and defining project scope:

• AI / ML leads in MedTech or research
• Clinicians involved in annotation or validation
• Technical leads designing evaluation and curation strategies

Why this matters

Datasets are not neutral collections of images. They are systems that already encode decisions about inclusion, labeling, grouping, and scope. Ignoring these decisions does not remove them. It only postpones their consequences.

Clarifying data due diligence early supports more defensible claims, prevents the high cost of "data debt," and reduces the likelihood of late-stage discoveries that invalidate months of R&D.

Strategic synergy

This document can be used independently or alongside the Data Due Diligence Protocol, which provides a practical, structured review tool. Together, they offer both a conceptual foundation and an operational framework.

Designed as a stand-alone framework or as the foundation for a deeper strategic partnership with VeraDP on R&D de-risking and experiment design.

Individual license: 245€

A bundle including both documents is available.

For team or corporate licensing: contact@veradp-ai.com