Tailor, Prashant D.

Background and Objective: This study aimed to characterize clinical characteristics and visual outcomes of patients with hemorrhagic choroidal detachments. Patients and Methods: Single-center retrospective case series of patients with hemorrhagic choroidal detachments from January 1, 2003, to December 31, 2020 at Mayo Clinic. Patients were categorized as appositional or non-appositional based on ultra-sonography. Results: Among 39 cases with hemorrhagic choroidal detachments, 15 (38%) were appositional and 24 (62%) were non-appositional. There was a significant difference between the appositional and non-appositional groups in mean initial logMAR visual acuity (VA) (2.8 [ SD = 0.20] vs 2.2 [ SD = 0.78], P < .001). However, no difference was observed in final logMAR VA (1.9 [ SD = 1.2] vs 1.5 [ SD = 1.1], P = .30). Comparison between surgical ( N = 9) versus non-surgical ( N = 5) hemorrhagic appositional choroidal detachments cases revealed no differences in outcomes. Comparison between anticoagulated ( N = 21) versus nonanticoagulated ( N = 18) patients with hemorrhagic choroidal detachments also revealed no differences in outcomes. Conclusion: Patients with appositional and nonappositional hemorrhagic choroidal detachments did not differ significantly with regards to visual outcome. Surgical intervention was not associated with improved final vision in this cohort.

Purpose of review Ophthalmology has emerged as the proving ground for medical artificial intelligence (AI) because of its imaging-centric workflows, standardized data acquisition, and well defined clinical endpoints. Recently, the field has started transitioning from narrowly trained, task-specific algorithms toward multimodal models that are trained on large datasets and can be adapted to many tasks. These models may integrate information from images, text, and clinical data and are increasingly trialed in real-world settings. This review examines recent advances and ongoing controversies in the use of AI as a basic science, translational, and clinical research tool in ophthalmology. Recent findings Recent literature highlights the growing role of self-supervised and foundation models for scalable phenotyping, biomarker discovery, and cross-task generalization, particularly in retinal imaging and optical coherence tomography (OCT). In parallel, large-scale clinical deployments, including cloud-based OCT platforms and autonomous diabetic retinopathy screening, demonstrate clinical impact while revealing persistent challenges related to workflow integration and patient generalizability. Regulatory and ethics-focused studies underscore increasing attention to validation rigor, bias, and postdeployment monitoring, exposing heterogeneity in research standards and physician practices. Summary Ophthalmic AI is maturing toward real-world clinical implementation. Future progress will depend less on performance gains alone and more on clinical alignment, rigorous validation, equity-aware evaluation, and postdeployment surveillance, alongside continued investment in basic science to advance disease understanding and inform robust, generalizable model development.

ImportanceSpecialized ophthalmology terminology limits comprehension for nonophthalmology clinicians and professionals, hindering interdisciplinary communication and patient care. The clinical implementation of large language models (LLMs) into practice has to date been relatively unexplored.ObjectiveTo evaluate LLM–generated plain language summaries (PLSs) integrated into standard ophthalmology notes (SONs) in improving diagnostic understanding, satisfaction, and clarity.Design, Setting, and ParticipantsRandomized quality improvement study conducted from February 1, 2024, to May 31, 2024, including data from inpatient and outpatient encounters in a single tertiary academic center. Participants were nonophthalmology clinicians and professionals and ophthalmologists. The single inclusion criterion was any encounter note generated by an ophthalmologist during the study dates. Exclusion criteria were (1) lack of established nonophthalmology clinicians and professionals for outpatient encounters and (2) procedure-only patient encounters.InterventionAddition of LLM-generated plain language summaries to ophthalmology notes.Main Outcomes and MeasuresThe primary outcome was survey responses from nonophthalmology clinicians and professionals assessing understanding, satisfaction, and clarity of ophthalmology notes. Secondary outcomes were survey responses from ophthalmologists evaluating PLS in terms of clinical workflow and accuracy, objective measures of semantic quality, and safety analysis.ResultsA total of 362 (85%) nonophthalmology clinicians and professionals (33.0% response rate) preferred the PLS to SON. Demographic data on age, race and ethnicity, and sex were not collected. Nonophthalmology clinicians and professionals reported enhanced diagnostic understanding (percentage point increase, 9.0; 95% CI, 0.3-18.2; P = .01), increased note detail satisfaction (percentage point increase, 21.5; 95% CI, 11.4-31.5; P &amp;lt; .001), and improved explanation clarity (percentage point increase, 23.0; 95% CI, 12.0-33.1; P &amp;lt; .001) for notes containing a PLS. The addition of a PLS was associated with reduced comprehension gaps between clinicians who were comfortable and uncomfortable with ophthalmology terminology (from 26.1% [95% CI, 13.7%-38.6%; P &amp;lt; .001] to 14.4% [95% CI, 4.3%-24.6%; P &amp;gt; .06]). PLS semantic analysis found high meaning preservation (bidirectional encoder representations from transformers score mean F1 score: 0.85) with greater readability than SONs (Flesch Reading Ease: 51.8 vs 43.6; Flesch-Kincaid Grade Level: 10.7 vs 11.9). Ophthalmologists (n = 489; 84% response rate) reported high PLS accuracy (90% [320 of 355] a great deal) with minimal review time burden (94.9% [464 of 489] ≤1 minute). PLS error rate on ophthalmologist review was 26% (126 of 489). A total of 83.9% (104 of 126) of errors were deemed low risk for harm and none had a risk of severe harm or death.Conclusions and RelevanceIn this study, use of LLM-generated PLSs was associated with enhanced comprehension and satisfaction among nonophthalmology clinicians and professionals, which might aid interdisciplinary communication. Careful implementation and safety monitoring are recommended for clinical integration given the persistence of errors despite physician review.

This cross-sectional study investigates the current landscape of generative artificial intelligence (GenAI) guidelines across ophthalmology journals.

Purpose of review Large language models (LLMs) are rapidly entering the landscape of medicine in areas from patient interaction to clinical decision-making. This review discusses the evolving role of LLMs in ophthalmology, focusing on their current applications and future potential in enhancing ophthalmic care. Recent findings LLMs in ophthalmology have demonstrated potential in improving patient communication and aiding preliminary diagnostics because of their ability to process complex language and generate human-like domain-specific interactions. However, some studies have shown potential for harm and there have been no prospective real-world studies evaluating the safety and efficacy of LLMs in practice. Summary While current applications are largely theoretical and require rigorous safety testing before implementation, LLMs exhibit promise in augmenting patient care quality and efficiency. Challenges such as data privacy and user acceptance must be overcome before LLMs can be fully integrated into clinical practice.

Purpose: This study investigates how Obstructive sleep apnea (OSA) affects the outcomes of ptosis repair. We hypothesized that patients with OSA have an increased rate of reoperation after ptosis repair. Methods: This retrospective cohort study included patients age >18 from the Mayo Clinic who underwent ptosis repair by levator advancement or Müller muscle-conjunctiva resection between 2018 and 2021. Outcomes were measured at 1 to 3 months of follow-up with surgical failure defined as asymmetry or unsatisfactory eyelid height requiring revision surgery within 1 year. Results: A total of 577 patients met the inclusion criteria. There was a statistically significant difference in surgical failure between patients with OSA and those without (20.5% vs. 13.1%, p = 0.02). Patients with OSA showed a statistically significant difference in risk of revision by a factor of 1.70 (95% CI: 1.06–2.07). Revisions were attributed to unsatisfactory eyelid height in 72.6% of patients and eyelid asymmetry in 21.1%. All patients who had revision surgery had satisfactory outcomes. On logistic regression analysis, when adjusting for age and sex, OSA was significantly associated with ptosis revision (p = 0.007). Conclusions: OSA increases risk of surgical failure and need for revision surgery in patients undergoing blepharoptosis repair but is not a sole risk factor.

A patient with cytomegalovirus viremia who underwent kidney transplant developed floaters in both eyes that did not improve after painful intravitreal injections of foscarnet and ganciclovir. What would you do next?

A 12-year-old female individual receives a diagnosis of SARS-CoV-2 and reports bilateral blurry vision, large blue paracentral scotomata, and a migraine without a scintillating scotoma. What would you do next?

Purpose: The purpose of this study was to characterize intradiploic dermoid and epidermoid orbital cysts to determine any differences in clinical, radiographic, or surgical features. Methods: A retrospective review was performed of patients presenting with intradiplopic dermoid or epidermoid cysts. Additionally, a complete review of the literature was performed to identify cases of intradiplopic orbital dermoid and epidermoid cysts. Data collected included age, sex, presenting symptoms, location of intradiplopic cyst, ophthalmic findings, treatment, and follow-up. Clinical features of dermoid versus epidermoid cyst were compared. Additionally, machine-learning algorithms were developed to predict histopathology based on clinical features. Results: There were 55 cases of orbital intradiploic cysts, 49 from literature review and six from our cohort. Approximately 31% had dermoid and 69% had epidermoid histopathology. Average age of patients with dermoid cysts was significantly lesser than that of patients with epidermoid cysts (23 vs. 35 years, respectively; P = 0.048). There was no difference between sex predilection, presenting symptoms, radiographic findings, or surgical treatment of dermoids and epidermoids. The majority of patients (64%) underwent craniotomy for surgical removal. Machine-learning algorithms KStar and Neural Network were able to distinguish dermoid from epidermoid with accuracies of 76.3% and 69%, respectively. Conclusion: Orbital intradiploic cysts are more commonly epidermoid in origin. Dermoid cysts presented in younger patients; however, there were no other significant differences in features including ophthalmic or radiographic findings. Despite similar features, machine learning was able to identify dermoid versus epidermoid with good accuracy. Future studies may examine the role of machine learning for clinical guidance as well as new surgical options for intervention.