ABSTRACT To compare the efficacy and safety of teprotumumab and tocilizumab in different stages of thyroid eye disease (TED) patients in the United Arab Emirates (UAE). Methods A retrospective analysis of 44 TED patients (22 teprotumumab, 2023–2025 at Sheikh Shakhbout Medical City and 22 tocilizumab, 2018–2024 at Sheikh Khalifa Medical City) was conducted. Outcomes included proptosis reduction (Hertel exophthalmometry), clinical activity score (CAS) improvement, diplopia improvement, quality of life (QoL) improvement (TED-QOL), recurrence rates, and adverse events. Mann-Whitney U tests and chi-square/Fisher’s exact tests were used (p < .05). Results Teprotumumab produced greater mean proptosis reduction (2.55 ± 1.01 mm vs 2.07 ± 1.07 mm, p = .04) and a larger mean CAS improvement (5.18 ± 1.99 vs 4.59 ± 1.82, p = .34). Diplopia improvement occurred in 78.6% vs 75.0% of patients (p = .82), while ≥1-grade improvement by Gorman criteria was achieved in 59.1% of each group (p = .61). Quality-of-life improvement averaged 4.2 ± 1.4 vs 3.4 ± 1.5 points (p = .18). Disease recurrence was lower with teprotumumab (4.5% vs 13.6%, p = .28). Adverse events occurred in 54.5% (teprotumumab) and 43.5% (tocilizumab) patients (p = .47), and were generally mild and reversible. Conclusion Teprotumumab and tocilizumab improved TED outcomes, with teprotumumab showing trends toward better proptosis reduction and lower recurrence. Larger studies are needed to confirm these findings.

Epiretinal membrane (ERM) is a prevalent vitreoretinal interface disorder characterized by fibro-cellular proliferation on the inner retinal surface. The prevalence increases markedly with age, and progressive membrane contraction distorts retinal architecture, producing metamorphopsia and irreversible visual impairment thereby significantly impairing patients' quality of life. This highlights the need for more accurate and efficient diagnostic and therapeutic strategies. Artificial intelligence (AI) has emerged as a promising tool to address such challenges. This review summarizes and evaluates recent studies on the applications of AI in ERM management, identifying current limitations and future research directions. A comprehensive literature search was conducted in PubMed, Web of Science, Embase, and Cochrane Library databases, focusing on studies related to AI and ERM published over the past decade. The findings indicate that while research on treatment and prognosis prediction remains limited, and new technologies are expected in image processing, current AI models demonstrate substantial potential in the ERM management, especially in detection and diagnosis. Nonetheless, current evidence is constrained by challenges such as single-center design, limited external validation across devices or ethnicities, insufficient multimodal imaging, and lack of health-economic or workflow integration data. Future multicenter prospective studies, federated learning platforms, publicly annotated imaging dataset and cost-effectiveness analyses are warranted to develop robust, generalizable models that can be seamlessly integrated into clinical workflows, substantially optimizing ERM management and delivering tangible benefits to both patients and clinical practice.