Comparative performance of chatgpt and gemini in diagnostic classification and clinical reasoning for open-angle glaucoma: a standardized scenario-based study
Jul 2026· International ophtalmology· Vol 46· 0 citations· 26 references
Medicine
TL;DR
Error pattern analysis indicated that Gemini was more prone to overdiagnosis and reasoning inconsistency, whereas GPT-5.3 was relatively conservative, and ChatGPT and Gemini both demonstrate certain capabilities in diagnosing POAG, but their stability on borderline cases remains limited.
LLM-based chatbots can provide acceptable surgical guidance for straightforward primary surgical cases, but their utility is limited in high-risk or complex clinical settings, and their rationale quality was significantly lower in complex scenarios than in primary ones.
Müge Toprak, Büşra Yılmaz Tuğan, N. Yüksel· Seminars in Ophthalmology· 0 citations
Large language model-based AI systems produced structured glaucoma-related reasoning with performance that overlapped with attending ophthalmologists but did not establish clinical equivalence, but may have potential as supervised decision-support and educational tools.
Hou-Fa Yin, Lixia Shen, Haiyan Cai et al.· Graefe's archive for clinica...· 0 citations
While both models show potential as clinical decision-support tools, Gemini 3.0 exhibited superior diagnostic performance in real-world otologic cases in the first study evaluating LLMs using real-world otologic data.
Bilge Tuna, Gokhan Tuzemen, Hasan Mutlu· BMC Medical Informatics and...· 0 citations
This study aimed to compare the diagnostic accuracy of multimodal Large Language Models (LLMS), namely, ChatGPT-5, ChatGPT-4o, and Gemini Pro 2.5, with board-certified oral medicine specialists. In this retrospective diagnostic accuracy study, 300 histopathologically confirmed salivary gland disease cases served as the...
Fatma E. A. Hassanein, Yousra Ahmed, Asmaa Abou-Bakr et al.· The Egyptian Journal of Otol...· 0 citations
While all four models exhibited comparable adherence to guideline-based content, DeepSeek-R1 demonstrated superior performance in supplementary informational value and readability, highlighting the importance of evaluating LLMs not only for accuracy but also for their capacity to enhance clinical communication and deci...
Li Xu, Xu Qiu, Jia-Yi Deng et al.· Neurology Asia· 0 citations
Clinicians should anticipate AI-driven treatment expectations and future work should explore prompting, patient sentiment, and multicentre moderators, as Frontier LLMs are highly reproducible internally but diverge from one another asymmetrically.
A. Narayan, F. Mariajoseph, Malik Farooq et al.· Neurosurgical review· 0 citations
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