Ritu Sampige

Background and Objective: Artificial intelligence (AI)-based tools in graduate medical education are increasingly utilized to support and augment learning due to their ability to provide tailored feedback to learners. With the growing use of AI in educational settings, there is a need to further understand the implications and utility of AI-based tools, particularly in the field of neuro-ophthalmology. With the increasing use of AI tools in neuro-ophthalmology, this narrative review provides an overview of the current and future use of AI for neuro-ophthalmic education. Methods: Two databases, PubMed and Google Scholar, were searched with the following keywords: Neuro-ophthalmology, Ophthalmology, Artificial Intelligence, Large Language Models, Deep Learning Models, Education, Medical Education, Teaching, Curriculum, Training, Diagnostics, and Diagnosis from May to October 2025. Inclusion criteria included peer-reviewed sources that were specific to AI and neuro-ophthalmology in the English language through 2025. Key Content and Findings: AI tools, including large language models (LLMs), deep learning models, and virtual reality (VR) devices, may be utilized to teach learners about creating a differential diagnosis, identifying diseases through imaging and diagnostics [e.g., fundus photography, optical coherence tomography (OCT), visual field patterns, radiology images, and understanding exam preparation questions]. Learners across various levels (i.e., medical students, residents, fellows) may interact with custom generative pre-training transformers (GPTs) in real-time to delve deeper into ophthalmic conditions and receive personalized feedback based on each learner’s knowledge gaps. Additionally, AI tools may harness the power of active learning methods through case generation and Socratic questioning. However, the caveat is that such tools vary in their knowledge base of neuro-ophthalmology concepts. Nonetheless, learners need to acknowledge the current limitations in the knowledge base of AI data sets and the need for human surveillance of AI-generated neuro-ophthalmic output, particularly through hybrid AI educational models. Conclusions: With the current shortage of neuro-ophthalmologists and the projected shortage of ophthalmologists overall, AI tools may serve as accessible, efficient, individualized, and accurate educational resources for learners across training levels, including in resource-limited regions.

Autosomal dominant optic atrophy (ADOA) is among the most prevalent inherited optic neuropathies with hallmark symptoms of bilateral, painless, progressive, and typically permanent vision loss over time. ADOA can affect patients’ quality of life with debilitating visual symptoms, and there is a pressing need for effective therapeutics. In this paper, we review the current and future investigational therapies for ADOA, including the use of intravitreal injections of antisense oligonucleotides through Targeted Augmentation of Nuclear Gene Output (TANGO), CRISPR-based therapy, genetic editing, gene replacement approaches, and idebenone, a small-molecule mitochondrial modulator. Additionally, we review clinical trials for ADOA treatment and opportunities for future research on ADOA therapeutics, including the utilization of mitochondria-targeted peptides and antioxidants, NAD+ boosters/metabolic support, mitophagy and fission-fusion modulators, and cell-based regenerative therapy. The use of emerging technology to compensate for OPA1 protein haploinsufficiency provides new and vast avenues for the management of this otherwise vision-altering disease. Increased awareness of therapeutics for ADOA will allow for patient counseling regarding treatment access via clinical trials and for underscoring the importance of genetically testing family members, who may be incidentally identified with ADOA in a timely manner for newly available therapies. While patients with ADOA typically have poor visual prognoses, there are increasing promising therapies with the potential for preserving and improving visual function.

In light of the potential effects of spaceflight on the anterior segment of the eye, there is a pressing need for anterior segment imaging to be available and accessible to monitor astronauts’ ocular health, including alterations to the cornea and lens. We aim to highlight the clinical basis and need for anterior segment imaging for astronauts. We explore the impacts of spaceflight-associated hazards, including microgravity and radiation, on astronauts’ risk of developing anterior segment pathology including risk of ocular trauma, infection, dry eye symptoms, cataracts, and possibly additional pathologies from increased radiation exposure. Such risks highlight the potential value that longitudinal assessment of anterior ocular structures would offer in future spaceflight missions. Specifically, anterior segment imaging would enable evaluations of corneal morphology, including longitudinal monitoring for microgravity-induced changes, and evaluation of interventions that aim to preserve anterior segment health during spaceflight. Lastly, non-invasive anterior segment imaging allows for unique insights into astronaut ocular health and can be performed routinely through modalities such as anterior segment optical coherence tomography (AS-OCT) and ultrasound biomicroscopy (UBM). We discuss these modalities and their implications for astronaut health during future spaceflight.