Related Experiment Video
Updated: Mar 10, 2026
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Assessing the capabilities of AI-based large language models (AI-LLMs) in interpreting histopathological slides and
Khanisyah E Gumilar1,2, Grace Ariani3, Priangga A Wiratama3
1Graduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Background:
Integrating artificial intelligence-based large language models (AI-LLMs) into medical and other scientific domains is increasingly recognized as a tool to support complex tasks, such as interpreting histopathology slides and scientific figures. AI-LLMs can simplify these processes by providing clearer explanations. By improving accessibility and comprehension, AI-LLMs can significantly assist healthcare professionals in diagnosing and therapy determination. Students and the public also find it easier to understand complex scientific concepts and images.
Objectives:
This study explores the capability of AI-LLMs in interpreting histopathological slides and scientific images. This study aims to evaluate the performance of AI-LLMs in supporting diagnostics and improving comprehension in biomolecular sciences.
Methods:
The study was divided into two parts: interpreting histopathology slides and scientific figures. Twelve histopathology images and twelve scientific figures were tested on each of the three most frequently used chatbots (ChatGPT-4, Gemini Advanced, and Copilot). Responses from the chatbots were coded and blindly examined by expert raters using five parameters-relevance, clarity, depth, focus, and coherence-on a 5-point Likert scale. Statistical analysis included one-way ANOVA and multiple linear regression.
Results:
ChatGPT-4 outperformed Gemini Advanced and Copilot in histopathology and scientific image interpretation (P < 0.001) with significantly higher scores across all parameters (relevance, clarity, depth, focus, and coherence). ChatGPT-4's superior performance may be due to its advanced algorithms, extensive training data, specialized modules, and user feedback.
Conclusions:
ChatGPT-4 excels in interpreting histopathology and scientific images, which may lead to improving diagnostic accuracy, clinical decision-making, and reducing pathologists' workload. It also benefits education by enhancing students' understanding of complex images and promoting interactive learning. ChatGPT-4 shows a significant potential to improve patient care and enrich student learning.
