Computational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data

Abstract

Pathology is undergoing a shift from morpho-molecular interpretation toward the computational integration of molecular mechanisms encoded in tissue architecture. Here, we argue that such morphology-driven molecular inference may enable biomarker prediction and potentially generate therapeutic insights directly from routine histology. This paradigm has important clinical implications for quantitative biomarker testing, patient stratification, and the design of digital biomarker-based clinical trials. At the same time, we emphasize that most current artificial intelligence (AI) models remain correlative, with clinical impact still dependent on rigorous validation, integration into workflows, and ethical governance. Addressing these open challenges will be essential for computational pathology to mature into a clinically meaningful discipline.

Keywords: artificial intelligence; biomarkers; deep learning; pathology.