ArticlePatterns (New York, N.Y.)2025
HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slides.Research square · 2026Article
- SHIELD: An AI Framework for Skeletal Health Intelligence and Early Lesion Detection to Improve Orthopedic Referrals.Journal of medical systems · 2026Article
- Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients.Research square · 2026Article
- Article
- HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data.Patterns (New York, N.Y.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.