A review of ophthalmology education in the era of generative artificial intelligence.

PURPOSE: To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions. DESIGN: A literature review and analysis of current AI applications and educational programs in ophthalmology. METHODS: Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies. RESULTS: Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students' education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology. CONCLUSIONS: Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

Hypertension Likely Drives Arteriolar Wall Thickening in Preclinical Diabetic Retinopathy While Diabetes Drives Wall Thickness in Clinical Retinopathy.

Purpose: Both hypertension and diabetes are known to increase the wall-to-lumen ratio (WLR) of retinal arterioles, but the differential effects are unknown. Here, we study the timing and relative impact of hypertension versus diabetes on the WLR in diabetic retinopathy (DR) to address this unresolved question. Methods: This prospective cross-sectional study compared the retinal arteriolar WLR in 17 healthy eyes, 15 with diabetes but no apparent DR (DM no DR), and 8 with diabetic macular edema (DME) and either nonproliferative or proliferative DR. We imaged each arteriole using adaptive optics scanning laser ophthalmoscopy and measured the WLR using ImageJ. Multiple linear regression (MLR) was performed to estimate the effects of hypertension, diabetes, and age on the WLR. Results: Both subjects with DM no DR and subjects with DME had significantly higher WLR than healthy subjects (0.36 ± 0.08 and 0.42 ± 0.08 vs. 0.29 ± 0.07, 1-way ANOVA P = 0.0009). MLR in healthy subjects and subjects with DM no DR showed hypertension had the strongest effect (regression coefficient = 0.08, P = 0.009), whereas age and diabetes were not significantly correlated with WLR. MLR in all three groups together (healthy, DM no DR, and DME) showed diabetes had the strongest effect (regression coefficient = 0.05, P = 0.02), whereas age and hypertension were not significantly correlated with WLR. Conclusions: Hypertension may be an early driver of retinal arteriolar wall thickening in preclinical DR, independent of age or diabetes, whereas changes specific to DR may drive wall thickening in DME and later DR stages. Translational Relevance: We offer a framework for understanding the relative contributions of hypertension and diabetes on the vascular wall, and emphasize the importance of hypertension control early in diabetes even before DR onset.

Imaging the Retinal Vascular Mural Cells In Vivo: Elucidating the Timeline of Their Loss in Diabetic Retinopathy.

BACKGROUND: Vascular mural cells (VMCs) are integral components of the retinal vasculature with critical homeostatic functions such as maintaining the inner blood-retinal barrier and vascular tone, as well as supporting the endothelial cells. Histopathologic donor eye studies have shown widespread loss of pericytes and smooth muscle cells, the 2 main VMC types, suggesting these cells are critical to the pathogenesis of diabetic retinopathy (DR). There remain, however, critical gaps in our knowledge regarding the timeline of VMC demise in human DR. METHODS: In this study, we address this gap using adaptive optics scanning laser ophthalmoscopy to quantify retinal VMC density in eyes with no retinal disease (healthy), subjects with diabetes without diabetic retinopathy, and those with clinical DR and diabetic macular edema. We also used optical coherence tomography angiography to quantify capillary density of the superficial and deep capillary plexuses in these eyes. RESULTS: Our results indicate significant VMC loss in retinal arterioles before the appearance of classic clinical signs of DR (diabetes without diabetic retinopathy versus healthy, 5.0±2.0 versus 6.5±2.0 smooth muscle cells per 100 µm; P<0.05), while a significant reduction in capillary VMC density (5.1±2.3 in diabetic macular edema versus 14.9±6.0 pericytes per 100 µm in diabetes without diabetic retinopathy; P=0.01) and capillary density (superficial capillary plexus vessel density, 37.6±3.8 in diabetic macular edema versus 45.5±2.4 in diabetes without diabetic retinopathy; P<0.0001) is associated with more advanced stages of clinical DR, particularly diabetic macular edema. CONCLUSIONS: Our results offer a new framework for understanding the pathophysiologic course of VMC compromise in DR, which may facilitate the development and monitoring of therapeutic strategies aimed at VMC preservation and potentially the prevention of clinical DR and its associated morbidity. Imaging retinal VMCs provides an unparalleled opportunity to visualize these cells in vivo and may have wider implications in a range of diseases where these cells are disrupted.

Hemodynamic Effects of Anti-Vascular Endothelial Growth Factor Injections on Optical Coherence Tomography Angiography in Diabetic Macular Edema Eyes.

Purpose: To evaluate retinal hemodynamic responses to anti-vascular endothelial growth factor (VEGF) injection in eyes with diabetic macular edema using optical coherence tomography angiography (OCTA). We performed a comparison of two different thresholding methods to identify the most accurate for studying the vessel density (VD) in diabetic macular edema eyes. Methods: The study prospectively included 26 eyes of 22 subjects (aged 60.2 ± 13.7 years) who underwent OCTA scan before and after anti-VEGF injection (mean interval between OCTA = 31.1 ± 17.3 days). We analyzed adjusted flow index, VD, and Skeletonized vessel length density in the parafoveal area (3-mm annulus with a 1-mm inner circle), along with full-thickness fovea avascular zone area and central foveal thickness (CFT). Using averaged scans VD as the ground truth, we compared two different algorithms for VD at the different plexuses. Longitudinal changes were assessed using a generalized linear model correcting for central foveal thickness and Q-score. Results: We found significantly decreased adjusted flow index in the DCP layer (P = 0.010) at the follow-up. Furthermore, foveal avascular zone (P < 0.001) and central foveal thickness (P = 0.003) showed significant decrease on follow-up compared with baseline. Comparing the thresholding algorithms showed that vessel length density-based thresholding was more accurate for quantifying the DCP VD. Conclusions: The adjusted flow index decreased significantly in the DCP layer on follow-up OCTA scan, suggesting vascular flow disruption and decreased deep retinal perfusion after anti-VEGF injection. Our results also highlight the fact that the choice of thresholding method is particularly critical for DCP quantification in eyes with diabetic macular edema. Translational Relevance: Findings confirmed impaired deep retinal capillary flow after anti-VEGF injection.

Natural Language Processing in Spine Surgery: A Systematic Review of Applications, Bias, and Reporting Transparency.

BACKGROUND: Natural language processing (NLP) is a discipline of machine learning concerned with the analysis of language and text. Although NLP has been applied to various forms of clinical text, the applications and utility of NLP in spine surgery remain poorly characterized. Here, we systematically reviewed studies that use NLP for spine surgery applications, and analyzed applications, bias, and reporting transparency of the studies. METHODS: We performed a literature search using the PubMed, Scopus, and Embase databases. Data extraction was performed after appropriate screening. The risk of bias and reporting quality were assessed using the PROBAST and TRIPOD tools. RESULTS: A total of 12 full-text articles were included. The most common diseases represented include spondylolisthesis (25%), scoliosis (17%), and lumbar disk herniation (17%). The most common procedures included spinal fusion (42%), imaging (e.g. magnetic resonance, X-ray) (25%), and scoliosis correction (17%). Reported outcomes were diverse and included incidental durotomy, venous thromboembolism, and the tone of social media posts regarding scoliosis surgery. Common sources of bias identified included the use of older methods that do not capture the nuance of a text, and not using a prespecified or standard outcome measure when evaluating NLP methods. CONCLUSIONS: Although the application of NLP to spine surgery is expanding, current studies face limitations and none are indicated as ready for clinical use. Thus, for future studies we recommend an emphasis on transparent reporting and collaboration with NLP experts to incorporate the latest developments to improve models and contribute to further innovation.