Arq. Bras. Oftalmol. 2026;89 (1 )
:1-8
| DOI: 10.5935/0004-2749.2024-0212
Abstract
PURPOSE: To analyze the association between corneal tomography patterns and atopic conditions in children and adolescents, and to investigate the relationship between corneal tomography findings, sleeping position, and dominant hand.
METHODS: Patients aged 8–16 yr underwent ocular and immunological examinations, including biomicroscopy, corneal tomography, the International Study of Asthma and Allergies in Childhood questionnaire, and an allergy skin test. Based on immunological results, participants were assigned to either the Control Group or the Atopic Group. Tomographic indices were analyzed alongside information on ocular itching, sleeping position, and dominant hand.
RESULTS: A total of 158 patients (mean age: 10.72 ± 2.13 yr) were evaluated, including 34 (21.52%) in the Control Group and 124 (78.48%) in the Atopic Group. Abnormal tomography was observed in 25 patients (15.82%), while 133 (84.18%) had normal results. Comparison between the Control and Atopic Groups regarding ocular itching episodes revealed a statistically significant difference (p≤0.05). Dominant hand and sleeping position showed no statistically significant associations with group classification, tomography results, or ocular itching.
CONCLUSION: Systemic allergies are strongly associated with biomechanical and structural corneal changes, which may or may not progress to different keratoconus patterns. No association was found between eye rubbing and any tomographic parameter, nor between sleeping position or hand dominance and tomography findings.
Keywords: Adolescent; Child; Hypersensitivity; Hypersensitivity, immediate; Cornea; Skin tests; Sleep; Tomography.
Arq. Bras. Oftalmol. 2026;89 (4 )
:1-6
| DOI: 10.5935/0004-2749.2025-0283
Abstract
PURPOSE: To assess the performance of a contemporary large language model (ChatGPT-5) against ophthalmology residents on a standardized set of glaucoma multiple-choice questions.
METHODS: We conducted a cross-sectional comparative study with 189 text-only glaucoma multiple-choice questions from the Cybersight question bank. ChatGPT-5 was tested under standardized conditions, with each item placed in a new chat and limited to letter-only outputs. Six ophthalmology residents from a Brazilian training program (two Postgraduate Year 1, two Postgraduate Year 2, and two Postgraduate Year 3) answered the same questions under supervision. Accuracy was calculated using the official key. McNemar’s exact test was used to compare items between ChatGPT-5 and residents, and matched odds ratios and 95% confidence intervals (95% CIs) were calculated using the Haldane–Anscombe correction.
RESULTS: ChatGPT-5 received 164 of 189 correct responses (86.8%; 95% CI, 81.2–90.9). Residents’ overall accuracy was 62.9% (713/1,134; 95% CI, 60.0–65.6). The top-performing resident earned 76.7%. ChatGPT-5 outperformed all residents in head-to-head comparisons, with odds ratios ranging from 1.84 (95% CI, 1.10–3.08) to 13.15 (95% CI, 5.93–29.20), all p≤0.023. ChatGPT-5 correctly answered 17/189 items (9.0%), but fewer than half of residents were correct (“large language model-only wins”), whereas residents were more successful on items that ChatGPT-5 overlooked.
CONCLUSIONS: ChatGPT-5 outperformed ophthalmology residents on text-based glaucoma multiple-choice questions, indicating its potential as a subspecialty education and assessment tool. Generalizability is limited by the single question bank, text-only items, a small resident cohort, and the evaluation of one large language model version at a single time point. Before incorporating these findings into clinical decision-making, larger, multimodal, and longitudinal studies are required.
Keywords: Glaucoma; Artificial intelligence; Large language models; Education, medical; Medical staff, hospital
ABO is licensed under a Creative Commons Attribution-NonComercial 4.0 Internacional.
