Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology. They are also increasingly being utilized in studies on ophthalmology-related exams, particularly those containing multiple-choice questions (MCQs). This narrative review evaluates both the opportunities and the challenges of integrating chatbots into ophthalmology research, with separate assessments of studies involving open- and close-ended questions. While chatbots have demonstrated sufficient accuracy in handling MCQ-based studies, supporting their use in education, additional exam security measures are necessary. The research on open-ended question responses suggests that AI-based LLM chatbots could be applied across nearly all areas of ophthalmology. They have shown promise for addressing patient inquiries, offering medical advice, patient education, supporting triage, facilitating diagnosis and differential diagnosis, and aiding in surgical planning. However, the ethical implications, confidentiality concerns, physician liability, and issues surrounding patient privacy remain pressing challenges. Although AI has demonstrated significant promise in clinical patient care, it is currently most effective as a supportive tool rather than as a replacement for human physicians.

Purpose: To describe and evaluate demographic, clinical features, prognostic factors, and rate of success of surgery and visual outcomes in patients with late presentation of retinal detachment. Methods: A retrospective, comparative, observational case series of patients with late presentation retinal detachment, defined as retinal detachment with the loss of central vision for 4 weeks or more, over a period of 12 months. Results: The mean of onset of central visual loss was 12.7 weeks (SD, 21.3). Proliferative vitreoretinopathy at the first operation was identified in 69% of eyes. The overall primary success rate was 69.2%, significantly less than that was found in outcomes for nonselected retinal detachment (primary success rate, 86%; P = 0.006). The initial best-corrected visual acuity was 20/500, and the final was 20/160 (P = 0.0027). There were no identifiable statistically significant socioeconomic factors related to late presentation. Conclusion: A high rate of established proliferative vitreoretinopathy on presentation was identified, and although cases can be treated with good anatomical results, visual outcomes are often less favorable. Primary surgical success is lower, and more reoperations are required compared with standard retinal detachments.

Purpose: To identify optical coherence tomography (OCT) biomarkers for macula-off rhegmatogenous retinal detachment (RRD) with artificial intelligence (AI) and to correlate these biomarkers with functional outcomes. Methods: Patients with macula-off RRD treated with single vitrectomy and gas tamponade were included. OCT volumes, taken at 4 to 6 weeks and 1 year postoperative, were uploaded on an AI-derived platform (Discovery OCT Biomarker Detector; RetinAI AG, Bern, Switzerland), measuring different retinal layer thicknesses, including outer nuclear layer (ONL), photoreceptor and retinal pigmented epithelium (PR + RPE), intraretinal fluid (IRF), subretinal fluid, and biomarker probability detection, including hyperreflective foci (HF). A random forest model assessed the predictive factors for final best-corrected visual acuity (BCVA). Results: Fifty-nine patients (42 male, 17 female) were enrolled. Baseline BCVA was 0.5 logarithmic minimum angle of resolution (logMAR) ± 0.1, significantly improving to 0.3 ± 0.1 logMAR at the final visit (P < 0.001). Average thickness analysis indicated a significant increase after the last follow-up visit for ONL (from 95.16 ± 5.47 µm to 100.8 ± 5.27 µm, P = 0.0007) and PR + RPE thicknesses (60.9 ± 2.6 µm to 66.2 ± 1.8 µm, P = 0.0001). Average occurrence rate of HF was 0.12 ± 0.06 at initial visit and 0.08 ± 0.05 at last follow-up visit (P = 0.0093). Random forest model revealed baseline BCVA as the most critical predictor for final BCVA, followed by ONL thickness, HF, and IRF presence at the initial visit. Conclusions: Increased ONL and PR-RPE thickness associate with better outcomes, while HF presence indicates poorer results, with initial BCVA remaining a primary visual predictor. Translational Relevance: The study underscores the role of novel biomarkers like HF in understanding visual function in macula-off RRD.