Background/Objectives: Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with its global prevalence projected to rise sharply as diabetes incidence increases. Early detection and timely management are critical to reducing DR-related vision loss. Optical Coherence Tomography Angiography (OCTA) now enables non-invasive, layer-specific visualization of the retinal vasculature, facilitating more precise identification of early microvascular changes. Concurrently, advancements in artificial intelligence (AI), particularly deep learning (DL) architectures such as convolutional neural networks (CNNs), attention-based models, and Vision Transformers (ViTs), have revolutionized image analysis. These AI-driven tools substantially enhance the sensitivity, specificity, and interpretability of DR screening. Methods: A systematic review of PubMed, Scopus, WOS, and Embase databases, including quality assessment of published studies, investigating the result of different AI algorithms with OCTA parameters in DR patients was conducted. The variables of interest comprised training databases, type of image, imaging modality, number of images, outcomes, algorithm/model used, and performance metrics. Results: A total of 32 studies were included in this systematic review. In comparison to conventional ML techniques, our results indicated that DL algorithms significantly improve the accuracy, sensitivity, and specificity of DR screening. Multi-branch CNNs, ensemble architectures, and ViTs were among the sophisticated models with remarkable performance metrics. Several studies reported that accuracy and area under the curve (AUC) values were higher than 99%. Conclusions: This systematic review underscores the transformative potential of integrating advanced DL and machine learning (ML) algorithms with OCTA imaging for DR screening. By synthesizing evidence from 32 studies, we highlight the unique capabilities of AI-OCTA systems in improving diagnostic accuracy, enabling early detection, and streamlining clinical workflows. These advancements promise to enhance patient management by facilitating timely interventions and reducing the burden of DR-related vision loss. Furthermore, this review provides critical recommendations for clinical practice, emphasizing the need for robust validation, ethical considerations, and equitable implementation to ensure the widespread adoption of AI-OCTA technologies. Future research should focus on multicenter studies, multimodal integration, and real-world validation to maximize the clinical impact of these innovative tools.

Gastrointestinal (GI) cancers arise in the GI tract and accessory organs, including the mouth, esophagus, stomach, liver, biliary tract, pancreas, small intestine, large intestine, and rectum. GI cancers are a major cause of cancer-related morbidity and mortality worldwide. Exosomes act as mediators of cell-to-cell communication, with pleiotropic activity in the regulation of homeostasis, and can be markers for diseases. Non-coding RNAs (ncRNAs), such as long non-coding RNAs (lncRNAs), can be transported by exosomes derived from tumor cells or non-tumor cells. They can be taken by recipient cells to alter their function or remodel the tumor microenvironment. Moreover, due to their uniquely low immunogenicity and excellent stability, exosomes can be used as natural carriers for therapeutic ncRNAs in vivo. Exosomal lncRNAs have a crucial role in regulating several cancer processes, including angiogenesis, proliferation, drug resistance, metastasis, and immunomodulation. Exosomal lncRNA levels frequently alter according to the onset and progression of cancer. Exosomal lncRNAs can therefore be employed as biomarkers for the diagnosis and prognosis of cancer. Exosomal lncRNAs can also monitor the patient’s response to chemotherapy while also serving as potential targets for cancer treatment. Here, we discuss the role of exosomal lncRNAs in the biology and possible future treatment of GI cancer.

Infectious keratitis (IK), which is one of the most common and catastrophic ophthalmic emergencies, accounts for the leading cause of corneal blindness worldwide. Different pathogens, including bacteria, viruses, fungi, and parasites, can cause IK. The diagnosis and etiology detection of IK pose specific challenges, and delayed or incorrect diagnosis can significantly worsen the outcome. Currently, this process is mainly performed based on slit–lamp findings, corneal smear and culture, tissue biopsy, PCR, and confocal microscopy. However, these diagnostic methods have their drawbacks, including experience dependency, tissue damage, cost, and time consumption. Diagnosis and etiology detection of IK can be especially challenging in rural areas or in countries with limited resources. In recent years, artificial intelligence (AI) has opened new windows in medical fields such as ophthalmology. An increasing number of studies have utilized AI in the diagnosis of anterior segment diseases such as IK. Several studies have demonstrated that AI algorithms can diagnose and detect the etiology of IK accurately and fast, which can be valuable, especially in remote areas and in countries with limited resources. Herein, we provided a comprehensive update on the utility of AI in IK.

Given the scarcity of data regarding ocular complications following COVID‐19 vaccination, the approach toward patients with suggestive symptoms and established clinical practice is lacking. Herein, we report the first case of herpetic endotheliithis and secondary stromal keratitis following inactivated COVID‐19 vaccination who experienced a relapse due to poor adherence.