Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models

TL;DR

This paper presents a novel AI approach combining retrieval-augmented large language models and pathology foundation models to improve the accuracy, consistency, and interpretability of thyroid cytology diagnosis.

Contribution

It introduces a new integrated AI framework that leverages RAG and domain-specific models for enhanced thyroid cytology interpretation, addressing existing challenges.

Findings

RAG-enhanced LLMs improve diagnostic accuracy and efficiency.

Pathology foundation models refine feature extraction from images.

The integrated approach achieves AUC 0.73-0.93 in predicting surgical pathology.

Abstract

Advancements in artificial intelligence (AI) are transforming pathology by integrat-ing large language models (LLMs) with retrieval-augmented generation (RAG) and domain-specific foundation models. This study explores the application of RAG-enhanced LLMs coupled with pathology foundation models for thyroid cytology diagnosis, addressing challenges in cytological interpretation, standardization, and diagnostic accuracy. By leveraging a curated knowledge base, RAG facilitates dy-namic retrieval of relevant case studies, diagnostic criteria, and expert interpreta-tion, improving the contextual understanding of LLMs. Meanwhile, pathology foun-dation models, trained on high-resolution pathology images, refine feature extrac-tion and classification capabilities. The fusion of these AI-driven approaches en-hances diagnostic consistency, reduces variability, and supports pathologists in dis-tinguishing benign from malignant thyroid lesions. Our results demonstrate that integrating RAG with pathology-specific LLMs significantly improves diagnostic efficiency and interpretability, paving the way for AI-assisted thyroid cytopathology, with foundation model UNI achieving AUC 0.73-0.93 for correct prediction of surgi-cal pathology diagnosis from thyroid cytology samples.