Evaluating multiple large language models on orbital diseases Article Swipe
Qichen Yang
,
Yan-Mei Zeng
,
Hong Wei
,
Cheng Chen
,
Qian Ling
,
Xiaoyu Wang
,
Xu Chen
,
Yi Shao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2025.1574378
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fcell.2025.1574378
The avoidance of mistakes by humans is achieved through continuous learning, error correction, and experience accumulation. This process is known to be both time-consuming and laborious, often involving numerous detours. In order to assist humans in their learning endeavors, ChatGPT (Generative Pre-trained Transformer) has been developed as a collection of large language models (LLMs) capable of generating responses that resemble human-like answers to a wide range of problems. In this study, we sought to assess the potential of LLMs as assistants in addressing queries related to orbital diseases. To accomplish this, we gathered a dataset consisting of 100 orbital questions, along with their corresponding answers, sourced from examinations administered to ophthalmologist residents and medical students. Five language models (LLMs) were utilized for testing and comparison purposes, namely, GPT-4, GPT-3.5, PaLM2, Claude 2, and SenseNova. Subsequently, the LLM exhibiting the most exemplary performance was selected for comparison against ophthalmologists and medical students. Notably, GPT-4 and PaLM2 demonstrated a superior average correlation when compared to the other LLMs. Furthermore, GPT-4 exhibited a broader spectrum of accurate responses and attained the highest average score among all the LLMs. Additionally, GPT-4 demonstrated the highest level of confidence during the test. The performance of GPT-4 surpassed that of medical students, albeit falling short of that exhibited by ophthalmologists. In contrast, the findings of the study indicate that GPT-4 exhibited superior performance within the orbital domain of ophthalmology. Given further refinement through training, LLMs possess considerable potential to be utilized as comprehensive instruments alongside medical students and ophthalmologists.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcell.2025.1574378
- https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1574378/pdf
- OA Status
- gold
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412066624
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Raw OpenAlex JSON
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https://openalex.org/W4412066624Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fcell.2025.1574378Digital Object Identifier
- Title
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Evaluating multiple large language models on orbital diseasesWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-07Full publication date if available
- Authors
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Qichen Yang, Yan-Mei Zeng, Hong Wei, Cheng Chen, Qian Ling, Xiaoyu Wang, Xu Chen, Yi ShaoList of authors in order
- Landing page
-
https://doi.org/10.3389/fcell.2025.1574378Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1574378/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1574378/pdfDirect OA link when available
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Psychology, Medicine, Computer science, Medical educationTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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