HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data

Artificial intelligence (AI) has the potential to greatly enhance diagnostic pathology, including the analysis of tissue samples to detect diseases such as colorectal cancer. This study explores how large language models (LLMs) and multimodal LLMs (MLLMs) can improve histopathological analysis by using medical data to aid diagnostics. However, challenges such as data quality and availability limit their effectiveness. To address these challenges, we introduce HistoChat, an AI-powered assistant designed to assist in colorectal cancer histopathology. It uses advanced techniques to improve data quality, such as generating image combinations and question-answer (QA) pairs to boost its learning. Despite working with limited data, HistoChat has significantly improved key metrics, including BLEU, ROUGE-L, and BERTScore, with an overall accuracy of 69.1% in human evaluation. These results suggest that HistoChat is a promising tool for enhancing diagnostic accuracy, especially in cases where data are scarce.