Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation

TL;DR

This paper reviews recent advancements in foundational AI models applied to endoscopy and pathology images for gastric inflammation detection, emphasizing their potential to improve early diagnosis and clinical outcomes.

Contribution

It provides a comprehensive overview of the principles, architectures, and emerging trends of foundation models in gastric endoscopy and pathology imaging, highlighting future research directions.

Findings

Foundation models enhance diagnostic accuracy in gastric endoscopy.

Emerging trends include multimodal data integration and real-time support.

Challenges involve data diversity and model robustness.

Abstract

The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FM), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FM in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice for prevention/management of GC cases, thereby improving patient outcomes.