# Bridging the Gap Between Static Histology and Dynamic Organ-on-a-Chip Models

## TL;DR

This paper reviews how Organ-on-a-Chip technology can bridge the gap between static tissue analysis and dynamic disease modeling, enabling better understanding and personalized treatment.

## Contribution

The paper introduces a new classification strategy for Organ-on-a-Chip models based on pathological mechanisms rather than organ type.

## Key findings

- Organ-on-a-Chip models can dynamically simulate disease processes like inflammation and fibrosis.
- More than 100 OOC disease models across multiple organs have been developed.
- New Pathophysiology integrates OOC, multi-omics, and AI to enable real-time disease tracking.

## Abstract

For more than a century, pathology has served as a cornerstone of modern medicine, relying primarily on static microscopic assessment of tissue morphology—such as H&E staining—which remains the “gold standard” for disease diagnosis. However, this conventional paradigm provides only a snapshot of disease states and often fails to capture their dynamic evolution and complex functional mechanisms. Moreover, animal models are constrained by marked interspecies differences, creating a persistent gap in translational research. To overcome these limitations, we propose the concept of New Pathophysiology, a research framework that transcends purely morphological descriptions and aims to resolve functional dynamics in real time. This approach integrates Organ-on-a-Chip (OOC) technology, multi-omics analyses, and artificial intelligence to reconstruct the entire course of disease initiation and to enable personalized medicine. In this review, we first outline the foundations and limitations of traditional pathology and animal models. We then systematically summarize more than one hundred existing OOC disease models across multiple organs—including the kidney, liver, and brain. Finally, we elaborate on how OOC technologies are reshaping the study of key pathological processes such as inflammation, metabolic dysregulation, and fibrosis by converting them into dynamic, mechanistic disease models, and we propose future perspectives in the field. This review adopts a relatively uncommon classification strategy based on pathological mechanisms (mechanism-based), rather than organ-based categorization, allowing readers to recognize shared principles underlying different diseases. Moreover, the focus of this work is not on emphasizing iteration or replacement of existing approaches, but on preserving past achievements from a historical perspective, with an emphasis on overcoming current limitations and enabling new advances.