Evidence map›Paper›PMID 41799031›Full record

ArticleBioMedicine2026

Assessing the capabilities of AI-based large language models (AI-LLMs) in interpreting histopathological slides and scientific figures: Performance evaluation study.

Khanisyah E Gumilar, Grace Ariani, Priangga A Wiratama, Rimbun, Tri H Yuliawati, Hong Chen, Ibrahim H Ibrahim, Cheng-Han Lin, Tai-Yu Hung, Dewanti Anggrahini and 8 more

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Article in BioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Khanisyah E GumilarGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Grace ArianiDepartment of Pathology Anatomy, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
Priangga A WiratamaDepartment of Pathology Anatomy, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
RimbunDepartment of Anatomy, Histology and Pharmacology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
Tri H YuliawatiDepartment of Anatomy, Histology and Pharmacology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
Hong ChenGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Ibrahim H IbrahimGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Cheng-Han LinGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Tai-Yu HungGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Dewanti AnggrahiniDepartment of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, ROC.
Arya S RajanagaraGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Khaled E OmranGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Zih-Ying YuDepartment of Public Health, China Medical University, Taichung, Taiwan, ROC.
Yu-Cheng HsuDepartment of Public Health, China Medical University, Taichung, Taiwan, ROC.
Erry G DachlanDepartment of Obstetrics and Gynecology, Universitas Airlangga Hospital - Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.
Jer-Yen YangGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.
Li-Na LiaoDepartment of Public Health, China Medical University, Taichung, Taiwan, ROC.
Ming TanGraduate Institute of Biomedical Science, China Medical University, Taichung, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI-LLMsArtificial intelligenceChatGPTCopilotGeminiHistopathological imageLarge language modelMedical diagnosticsScientific figure

Identifiers

PMID41799031
PMCPMC12962759

What Socratic holds

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LicenceCC BY
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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 Socratic graph.