Gumilar, Khanisyah E. and Ariani, Grace and Wiratama, Priangga A. and Rimbun and Yuliawati, Tri H. and Chen, Hong and Ibrahim, Ibrahim H. and Lin, Cheng-Han and Hung, Tai-Yu and Anggrahini, Dewanti and Rajanagara, Arya S. and Omran, Khaled E. and Yu, Zih-Ying and Hsu, Yu-Cheng and Dachlan, Erry G. and Yang, Jer-Yen and Liao, Li-Na and Tan, Ming (2026) Assessing the capabilities of AI-based large language models (AI-LLMs) in interpreting histopathological slides and scientific figures: Performance evaluation study. BioMedicine (Taiwan), 16 (1). 41 - 52.

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Abstract

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. © the Author(s).

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