Diagnostics and Biomarker Translation

Published

May 25, 2026

Diagnostics and biomarker translation deserve a separate chapter because discovery biomarkers often fail at the measurement and context-of-use step. The existing real-world evidence chapter covers causal evidence and biomarker use in therapeutic development; this chapter focuses on the assay, diagnostic, and qualification pathway.

Learning Objectives
  • Distinguish discovery biomarkers from validated assays and qualified biomarkers
  • Identify context of use as the core translation artifact
  • Separate analytical validity, clinical validity, and clinical utility
  • Recognize when a model feature is not a biomarker
  • Connect companion diagnostics to therapeutic-development decisions

AI biomarker discovery is only the start. Translation requires a defined context of use, a reliable measurement method, analytical validation, clinical validation, and a decision that the biomarker supports. A feature learned by a model is not automatically a biomarker. A biomarker associated with outcome is not automatically a diagnostic. A diagnostic model is not automatically a companion diagnostic.

Introduction

The FDA Biomarker Qualification Program frames biomarker development around a well-defined context of use, reliable measurement, and analytical performance that supports that context (FDA, 2026). That discipline is exactly what AI biomarker discovery often lacks. A model may find a molecular, histologic, imaging, digital, or multi-omic feature associated with outcome, but translation depends on whether the feature can be measured reliably and used for a defined decision.

This chapter exists to prevent duplication and confusion. The Real-World Evidence and Biomarker AI chapter covers evidence generation using real-world data and biomarkers in therapeutic development. This chapter covers diagnostic and assay translation.

Demonstrated

Demonstrated capability includes AI-supported biomarker discovery in bounded datasets when the feature, assay, population, endpoint, and validation cohort are specified.

Demonstrated capability also includes AI-assisted diagnostic assay development when analytical performance is evaluated against an appropriate reference standard and the intended use is clear.

Theoretical

Theoretical capability includes models that discover biomarkers that generalize across assay platforms, cohorts, sites, and treatment contexts. This is plausible in some domains, but platform shift and cohort bias remain common failure modes.

Theoretical capability also includes companion-diagnostic discovery from multimodal data. The decision standard is high because the diagnostic affects treatment selection.

Beyond current capabilities

Beyond current capabilities includes treating opaque model features as qualified biomarkers without measurement validity, biological interpretation, or context-of-use evidence.

Beyond current capabilities also includes assuming that model discrimination in a discovery cohort establishes clinical utility. Clinical utility requires a decision and evidence that acting on the result improves that decision.

Practice Notes

  • Write the context of use before reviewing model performance.
  • Separate analytical validity, clinical validity, and clinical utility.
  • Name the specimen, assay, platform, endpoint, and threshold.
  • Do not call a model feature a biomarker until measurement is defined.
  • Link companion-diagnostic claims to therapeutic decisions.