Microinvasive glaucoma surgery (MIGS) is emerging as a new therapeutic option for glaucoma patients who wish to reduce their medication burden and avoid the postoperative complications of conventional glaucoma filtration surgery. These devices differ in terms of their efficacy and safety profile. Schlemm’s canal devices have the most favorable safety profile at the compromise of modest efficacy, while subconjunctival and suprachoroidal devices are potentially more effective at lowering the intraocular pressure at the expense of a higher rate of complications. This review consolidates the latest evidence on the efficacy and safety of the MIGS devices in clinical use and provides an overview on upcoming devices which would likely also become viable treatment options in the near future. These clinical data would assist a glaucoma surgeon in selecting the most appropriate MIGS device for each patient based on the glaucoma severity and patient expectations.

Background Large language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and projects future directions for this burgeoning field. Methods We systematically searched across various databases (PubMed, Europe PMC, Scopus and Web of Science) for articles related to LLM use in ophthalmology, published between 1 January 2022 and 31 July 2023. Selected articles were summarised, and categorised by type (editorial, commentary, original research, etc) and their research focus (eg, evaluating ChatGPT’s performance in ophthalmology examinations or clinical tasks). Findings We identified 32 articles meeting our criteria, published between January and July 2023, with a peak in June (n=12). Most were original research evaluating LLMs’ proficiency in clinically related tasks (n=9). Studies demonstrated that ChatGPT-4.0 outperformed its predecessor, ChatGPT-3.5, in ophthalmology exams. Furthermore, ChatGPT excelled in constructing discharge notes (n=2), evaluating diagnoses (n=2) and answering general medical queries (n=6). However, it struggled with generating scientific articles or abstracts (n=3) and answering specific subdomain questions, especially those regarding specific treatment options (n=2). ChatGPT’s performance relative to other LLMs (Google’s Bard, Microsoft’s Bing) varied by study design. Ethical concerns such as data hallucination (n=27), authorship (n=5) and data privacy (n=2) were frequently cited. Interpretation While LLMs hold transformative potential for healthcare and ophthalmology, concerns over accountability, accuracy and data security remain. Future research should focus on application programming interface integration, comparative assessments of popular LLMs, their ability to interpret image-based data and the establishment of standardised evaluation frameworks.

Introduction: This study aimed to determine the reproducibility and usability of anterior segment images taken from a smartphone stabilised on a slit-lamp with those taken from a custom-mounted slit-lamp camera. Materials and Methods: This was a prospective, single-blind comparative digital imaging validation study. Digital photographs of patients with cataract were taken using a smartphone camera (an iPhone 5) on a telescopic mount and a Canon EOS 10D anterior segment camera. Images were graded and compared according to the Lens Opacification Classification System III (LOCS III). Results: A total of 440 anterior segment images were graded independently by 2 ophthalmologists, 2 residents and 2 medical students. Intraclass correlation (ICC) between the iPhone and anterior segment camera images were fair for nuclear opalescence (NO) and nuclear colour (NC), and excellent for cortical (C) and posterior subcapsular (PSC) (NO: ICC 0.40, 95% CI, 0.16 to 0.57; NC: ICC 0.47, 95% CI, 0.16 to 0.66; C: ICC 0.76, 95% CI, 0.71 to 0.81; PSC: ICC 0.81, 95% CI, 0.76 to 0.85). There was no difference in grader impression of confidence and images usability between both cameras (P = 0.66 and P = 0.58, respectively). Conclusion: Anterior segment images taken from an iPhone have good reproducibility for retro-illuminated images, but fair reproducibility for NO and NC under low light settings. There were no differences in grader confidence and subjective image suitability. Key words: Cataract, Clinical Ophthalmology, iPhone