METHODS: We did a secondary analysis of 233 participant-level population-based survey datasets (Rapid Assessment of Avoidable Blindness [RAAB] surveys and other population-based cross-sectional surveys) from 68 countries (2003-24) to estimate eCSC with a cataract surgical threshold of worse than 6/18 pinhole visual acuity and a threshold of 6/18 or better for postoperative presenting visual acuity (eCSC6/18) for each country. Eligible studies included people aged 50 years and older, without any further age restrictions. eCSC was calculated as the number of people who had received surgery and had a good visual outcome as a proportion of all people who had received or were still to receive surgery. For countries with only one survey estimate available, we reported the survey as the country estimate; for each country with two or more surveys, we used more recent and nationally representative survey to generate estimates. We included 228 of 233 datasets (excluding five datasets from the European region) in a mixed-effects logistic regression to estimate global and WHO regional temporal trends in eCSC6/18 from 2000 to 2030. For global estimates, we used eCSC in the Americas region as a proxy for the European region. We used 120 RAAB surveys that measured visual acuity at the 6/12 threshold to analyse causes of non-good outcomes (postoperative presenting visual acuity worse than 6/12) and estimated potential gains in eCSC (with a cataract surgical threshold of worse than 6/12 pinhole visual acuity and a 6/12 or better threshold for postoperative presenting visual acuity [eCSC6/12]) from correcting residual postoperative refractive errors. Potential gains were estimated by comparing eCSC6/12 calculated using postoperative presenting visual acuity with eCSC6/12 calculated using pinhole visual acuity (a proxy for visual acuity with optical correction of refractive error).

FINDINGS: We used 130 studies to report 68 country estimates of eCSC6/18. Country estimates for eCSC6/18 from surveys ranged from 2·1% (95% CI 0·9-3·4) in Burundi (2024) to 77·7% (72·9-82·5) in Qatar (2023). Globally, we predicted eCSC6/18 of 48·2% (39·7-57·2) in 2025, increasing by 8·4 percentage points (8·1-8·6) between 2020 and 2030 (from 43·9% [36·6-51·2] to 52·3% [41·4-62·7]). Uncorrected refractive error accounted for a median 26·4% of non-good outcomes per survey; we estimated that treating this condition could yield a median 3·7 percentage-point gain in eCSC6/12 globally.

INTERPRETATION: Global efforts are not on track to meet the target of a 30 percentage-point increase in eCSC between 2020 and 2030-immediate and substantial investment in cataract services is, therefore, required. Survey data indicated which elements of the cataract care pathway could be targeted for quality-improvement initiatives. A suite of cataract surgery quality indicators should be collected to better inform service providers.

METHODS: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.

RESULTS: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10-12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63-0.83.