Cannabis is the most used drug worldwide with an estimated 219 million users. This narrative review aims to explore the adverse effects and therapeutic applications of cannabis and cannabinoids on the eye, given its growing clinical and non-clinical uses. The current literature reports several adverse ocular effects of cannabis and cannabinoids, including eyelid tremor, ptosis, reduced corneal endothelial cell density, dry eyes, red eyes, and neuro-retinal dysfunction. Cannabinoids may transiently impair night vision, depth perception, binocular and monocular contrast sensitivity, and dynamic visual acuity. Cannabinoids are not currently considered a first-line treatment option for any ocular conditions. Δ-9-tetrahydrocannabinol been shown to result in short-term intraocular pressure reduction, but insufficient evidence to support its use in treating glaucoma exists. Potential therapeutic applications of cannabinoids include their use as a second-line agent for treatment-refractory blepharospasm, for dry eye disease given corneal anti-inflammatory properties, and for suppression of pendular nystagmus in individuals with multiple sclerosis, which all necessitate further research for informed clinical practices.

In pursuit of health equity, the World Health Organization has recently called for more extensive monitoring of inequalities in eye health. Population-based eye health surveys can provide this information, but whether underserved groups are considered in the design, implementation, and reporting of surveys is unknown. We conducted a systematic methodological review of surveys published since 2000 to examine how many population-based eye health surveys have considered underserved groups in their design, implementation, or reporting. We identified all population-based cross-sectional surveys reporting the prevalence of objectively measured vision impairment or blindness. Using the PROGRESS + framework to identify underserved groups, we assessed whether each study considered underserved groups within 15 items across the rationale, sampling or recruitment methods, or the reporting of participation and prevalence rates. 388 eye health surveys were included in this review. Few studies prospectively considered underserved groups during study planning or implementation, for example within their sample size calculations (n = 5, ∼1%) or recruitment strategies (n = 70, 18%). The most common way that studies considered underserved groups was in the reporting of prevalence estimates (n = 374, 96%). We observed a modest increase in the number of distinct PROGRESS + factors considered by a publication over the study period. Gender/sex was considered within at least one item by 95% (n = 367) of studies. Forty-three percent (n = 166) of included studies were conducted primarily on underserved population groups, particularly for subnational studies of people living in rural areas, and we identified examples of robust population-based studies in socially excluded groups. More effort is needed to improve the design, implementation, and reporting of surveys to monitor inequality and promote equity in eye health. Ideally, national-level monitoring of vision impairment and service coverage would be supplemented with smaller-scale studies to understand the disparities experienced by the most underserved groups.

The rapid integration of machine learning in ophthalmology may exacerbate existing disparities in eye health among underserved populations. This study aimed to assess the representation and quality of reporting on equity-relevant sociodemographic characteristics in retinal studies utilizing artificial intelligence (AI). A systematic scoping review. A comprehensive literature search was conducted using Ovid MEDLINE, Ovid Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) from January 1, 2018, to July 29, 2023. The reporting of equity-relevant sociodemographic characteristics of participants was assessed using the PROGRESS equity framework. The proportion of AI-based retinal studies reporting sociodemographic characteristics (e.g., race, ethnicity, gender, socioeconomic status), geographic diversity of dataset sources, and quality of reporting of racial and ethnic data. A total of 360 studies met inclusion criteria, the majority of which were retrospective (79.2%), imaging-based (99.4%) studies utilizing fundus photos (51.9%) or optical coherence tomography (43.1%) data from online databases (55.6%) or clinical charts (42.2%). Geographic diversity of studies was minimal, with only a few of the studies using clinical chart data that was collected in countries within the African (0.3%), Eastern Mediterranean (5.0%), and South-East Asian Region (6.7%). Sociodemographic reporting was limited, with 30.8% of studies reporting gender or sex, and only 8.9% reporting race or ethnicity of participants. Only 11.1% of studies tested AI algorithm performance by gender or sex, and 1.7% by race or ethnicity. The quality of racial and ethnic data reporting was poor overall, with significant gaps in how data was collected, determined, and classified. AI-related retinal studies have limited equity considerations, particularly with regards to underrepresentation of diverse sociodemographic groups and insufficient racial and ethnic data reporting. Enhanced diversity in study populations and improved data collection and reporting methodologies are needed to ensure AI algorithms are trained and perform comparably across heterogenous populations affected by retinal diseases.