Evidence map›Paper›PMID 42015052›Full record

ArticleBMC gastroenterology2026

Compare the features of several mainstream AI software programmes: explore their accuracy and repeatability in solving queries related to pancreatic cysts.

Xiaoyi Zheng, Yongkang Lai, Jinhui Yi, Zhaoshen Li, Jiulong Zhao, Lianghao Hu

Abstract readComparative Study
In one paragraph

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

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiaoyi Zheng *Shanghai Gongli Hospital, Ningxia medical university, Shanghai, 200135, China.
Yongkang Lai *Department of Gastroenterology, Medical College, Ganzhou People's Hospital, Nanchang University, No.16 Meiguan Avenue, Ganzhou City, Nanchang, Jiangxi Province, 341000, China.
Jinhui YiDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, P. R. China.
Zhaoshen LiDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, P. R. China. zhaoshen-li@hotmail.com.
Jiulong ZhaoDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, P. R. China. jlzhao9@163.com.
Lianghao HuDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, No. 168 Changhai Road, Shanghai, 200433, P. R. China. lianghao-hu@smmu.edu.cn.

Funding

National Natural Science Foundation of China Grant No. 82230018
6 · The paper itself

Abstract

backgroundThe prevalence of pancreatic cysts, a rare disease of the pancreas, has been increasing annually with advances in medical technology. However, health management education for such rare diseases is often inadequate, resulting in risky medical activities, patient confusion and concern. The development of large-scale language models (LLM) has provided a new platform for addressing these issues, with ChatGPT-4.0 and Deepseek-V3.1 being the most commonly used. In recent years, Libre Chat-v0.7.2 and Metaso AI-0.99 have gradually attracted widespread attention from scholars. Assessing the accuracy and comprehensiveness of such tools in addressing issues related to pancreatic cysts is crucial for their dissemination and application.

methodsNineteen questions related to pancreatic cysts were screened for final inclusion and categorized into four domains: basic, diagnostic, treatment and prevention. The questions were also submitted to ChatGPT-4.0, Deepseek-V3.1, Metaso AI-0.99, and Libre Chat-v0.7.2 for answers. The prompts included: specific inquiries, a word limit of 300 characters, and a requirement for detailed bullet points to present the results more clearly. We simultaneously submitted the questions and requirements to Glass Health, a specialized medical payment AI, and invited eleven pancreatic disease specialists to independently score the AI’s results based on their expertise. The analysis was conducted based on these evaluations.

resultsThese four artificial intelligence platforms generally provide accurate responses to questions concerning pancreatic cysts, though some answers may be incomplete or lack authoritative backing. When combined with assessments from eleven specialists, these large language model platforms score higher in addressing preventive measures for pancreatic cysts. Among these, Deepseek-V3.1 achieved a higher overall score than the other three platforms, demonstrating superior performance in pancreatic disease knowledge and patient comprehension, while also integrating cross-disciplinary insights for comprehensive responses. Similar to Glass Health, Metaso AI-0.99 conducts thorough online resource searches for each query and provides links to original literature. Users can explore these sources for further understanding. However, AI platform responses rely heavily on user-provided prompts, and users exhibit significant variations in medical comprehension. Future development requires updating big data models to achieve disease specificity by classifying user backgrounds, thereby delivering more appropriate responses.

conclusionsNew large language model LLM tools may provide patients and healthcare professionals with basic and accurate information for the health management of pancreatic cysts. Further optimization and updating of training models will be required in the future to promote the application of the internet in the health management of rare and uncommon diseases and to enhance the practicality of LLM tools in clinical practice.

Indexed as

Artificial IntelligencePancreatic CystSoftwareGenerative Artificial IntelligenceHumansLarge Language ModelsReproducibility of ResultsHealth managementLarge-scale language modelingPancreatic cysts

Identifiers

PMID42015052
PMCPMC13231655

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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 OpenQuestion graph.