With the rapid advancement of large language models, both general-purpose and ophthalmology-specific variants have shown substantial potential for ophthalmic image analysis. However, their task-specific effectiveness and applicable boundaries in clinical practice remain to be fully clarified. This review outlines the technical evolution of large models in ophthalmology, including masked visual modeling, vision-language contrastive learning, and subsequent stages of knowledge integration and multimodal reasoning. The performance of general-purpose versus ophthalmology-specific models across common clinical scenarios, including disease screening and initial triage, disease grading and lesion segmentation, and medical report generation with multimodal reasoning were further compared. Results show that general-purpose models are well-suited for multi-disease screening and primary-care triage, whereas ophthalmology-specific models excel in fine-grained grading, subtle lesions detection, and rare disease diagnosis. Moreover, instruction-tuned domain-specific models produce medical reports with greater clinical consistency. These findings provide practical guidance for selecting appropriate model types and technical pathways tailored to specific ophthalmic tasks. Furthermore, the review identifies the current challenges facing these models, including reliability, generalizability, evaluation frameworks, and reproducibility, as well as the resulting limitations in clinical application.

Objective To establish a suitable model for predicting the success of trial of labor after cesarean section (TOLAC) during the pregnancy at term.Methods Data for all deliveries at term with one cesarean delivery history in Shenzhen Maternity and Child healthcare hospital during 1 January 2012 to 31 December 2014 were reviewed.Variables associated with VBAC were identified and used to build a model to predict the outcome of TOLAC with multivariable logistic regression.Godness of fit and accuracy of the model were evaluated by ROC.Results A total of 531 women met inclusion criteria and underwent TOLAC.Of the women who underwent trial of labor, 448 (84.4％) had a successful VBAC, 83 failed, and 2 (0.38％) had uterine rupture.Multivariable logistic regression analysis showed that previous cesarean section (CS) time interval, neonatal birth weight (BW) and premature rupture of membranes (PROM) were independent factors affecting TOLAC outcome, and their Odds Ratios were 2.79, 1.002 and 0.244, respectively.The Logistic regression model was expressed as follows:P =1/[1 + exp (2.4 × neonatal BW + 1.03 × previous CS time interval-1.41 × PROM-10.24)].The Hosmer-lemeshow test showed that the model fitted well (x2 =123.45, P =0.996), and the prediction accuracy of the model was 86.77％.The model performed well with an AUC of 0.794 (P =0.000).Conclusions A predictive model, which contains three variables (previous CS time interval, neonatal BW and PROM), has been developed and its prediction efficiency and accuracy are satisfactory.The larger birth weight, the longer time interval from previous CS, and the absence of PROM are more likely to be failed in TOLAC.

OBJECTIVETo establish the growth curve of Siraitia grosvenorii and analyze the correlation between seed and growth of fruit.

METHODThe growth curve fitting function was applied for the study of correlation between seed and fruit growth.

RESULTThe significant positive correlation existed between seed and horizontal diameter x vertical diameter, not between seed and flesh weight of single fruit.

CONCLUSIONThe growth curve is a reciprocal function, and seed is one of major factors to influencing the size of fruits and the shape of fruit.

Cucurbitaceae ; growth & development ; Fruit ; growth & development ; Seeds ; growth & development ; Time Factors