Aug 2026· Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie· 0 citations· 21 references
Medicine
TL;DR
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.
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
When evaluated using real-world rhinologic cases integrating clinical, endoscopic, and written CT reports findings, a large language model demonstrated strong diagnostic agreement for CRSwNP and consistently reliable management recommendations, however, further validation is required before routine clinical implementat...
N. Alanazi, Tariq Abdulaziz Aldawood, Abdulrahman I. Alfayez et al.· European Archives of Oto-Rhi...· 0 citations
The VLM demonstrated reliable clinical interpretation and an acceptable safety profile, however its integration into clinical workflows for early recognition of physiological deterioration and patient acuity assessment requires further rigorous evaluation and comparison to currently used track-and-trigger systems and p...
I. Strechen, P. Krishnan, O. Kilickaya et al.· International Journal of Med...· 0 citations
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.
Zhe-Wen Zhang, Siyu Lu, Zhen-Qiang Xu et al.· International ophtalmology· 0 citations
A reasoning-driven vision-language framework that explicitly models the ophthalmologist's diagnostic workflow by generating structured clinical reasoning prior to diagnosis is developed, demonstrating that explicitly modeling expert clinical reasoning simultaneously improves interpretability and diagnostic performance.
Artificial intelligence in ophthalmology encounters a continual challenge: Systems proficient in picture classification seldom yield quantifiable enhancements in patient outcomes. The primary concern is the disparity between pixel-level performance metrics and their clinical significance. Primary obstacles encompass da...
Marco Zeppieri, Matteo Capobianco, Federico Visalli et al.· World Journal of Methodology· 0 citations
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