ArticleTranslational vision science & technology2026
Assessing the Accuracy of Artificial Intelligence-Generated Clinical Summaries From Ambulatory Glaucoma Subspecialty Clinical Encounters.
Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Comparative evaluation of four large language models (ChatGPT-5, Gemini 2.5 Pro, Claude Sonnet-4, and DeepSeek-R1) for diagnostic and therapeutic decision-making in real-world otolaryngology scenarios.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial intelligence in glaucoma diagnosis and management: Has its time come?Indian journal of ophthalmology · 2026Article
- The AI-Augmented Ophthalmologist for chronic ocular diseases: a patient-centered framework for human-AI collaboration.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
Purpose: The purpose of this study was to evaluate the accuracy of large language model (LLM) LLaMA 2-70B in summarizing glaucoma clinic notes into patient-friendly language and generating educational material. Methods: A random sample of 147 clinic notes from unique patients who visited Glaucoma Service at a tertiary center was analyzed. LLaMA 2 generated paragraph and bullet-point summaries in five subjects: (1) glaucoma diagnosis and type, (2) disease progression, (3) treatment plan, (4) treatment changes, and (5) surgical/laser interventions. Two ophthalmologists reviewed responses for accuracy and categorized them as "correct," "partially correct," or "incorrect." Discrepancies were adjudicated by a glaucoma specialist. A comparison using identical prompts was performed on a subset (n = 50) with ChatGPT-4. Results: LLaMA 2 correctly summarized 97 notes (66%) in paragraph and 103 (70%) in bullet format. Another 44 (30%) and 41 (28%) were partially correct, respectively. Paragraph summaries were more accurate and complete for glaucoma suspects than diagnosed patients (82% vs. 53%, P < 0.001). For targeted clinical questions, LLaMA 2 accurately identified glaucoma diagnosis in 118 notes (80%), disease stability/progression in 129 (88%), treatment plans in 127 (87%), treatment changes in 134 (91%), and surgical/laser interventions in 124 (84%). ChatGPT-4 achieved 46% correct paragraph summaries, 50% correct bullet summaries, and accuracies of 96%, 88%, 64%, 78%, and 82%, respectively, for targeted questions. Conclusions: Although LLaMA 2 is not yet reliable as a standalone clinical tool, it shows promise to improve clinical communication. Translation Relevance: LLMs may enhance patient experience and health literacy by standardizing patient-friendly language in clinical care.
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