ReviewFrontiers in cell and developmental biology2025
Artificial intelligence technology in ophthalmology public health: current applications and future directions.
Review in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Next-gen vision: a systematic review on robotics transforming ophthalmic surgery.Journal of robotic surgery · 2025Pooled it
- Automated Optic Disc Tilt Classification in Fundus Photographs Using Segmentation and the Elliptical Ratio: External Clinical Validation Study.JMIR formative research · 2026Article
- Long-Term Clinical Outcomes of nDSAEK and Machine Learning-Based Prediction of Graft Survival in Corneal Endothelial Decompensation.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Checkpoint inhibition and beyond: Precision immune engineering for the immune-privileged landscape of ocular malignancies.BioImpacts : BI · 2026Review
- Perceived Trust in Artificial Intelligence in Eye Care: Demographic Determinants and Variations in Attitudes Among Ophthalmologists and Residents.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Prediction of refractive error in adolescents using a multimodal large language model.Frontiers in medicine · 2026Article
- Artificial intelligence driven transformation of pediatric eye health education based on bibliometric analysis and a cross-sectional survey.Frontiers in public health · 2026Article
- Social Media and Telemedicine in Ophthalmology: Misinformation, Patient Perception, and the Evolving Digital Patient.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- [Neurodegeneration and retinal changes-A literature overview].Zeitschrift fur Gerontologie und Geriatrie · 2025Review
- Mechanisms and Evolution of Antimicrobial Resistance in Ophthalmology: Surveillance, Clinical Implications, and Future Therapies.Antibiotics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- From Pathophysiology to Innovative Therapies in Eye Diseases: A Brief Overview.International journal of molecular sciences · 2025Review
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- ePWV as a scalable risk factor for large-scale glaucoma screening: evidence from a national Chinese cohort.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global eye health has become a critical public health challenge, with the prevalence of blindness and visual impairment expected to rise significantly in the coming decades. Traditional ophthalmic public health systems face numerous obstacles, including the uneven distribution of medical resources, insufficient training for primary healthcare workers, and limited public awareness of eye health. Addressing these challenges requires urgent, innovative solutions. Artificial intelligence (AI) has demonstrated substantial potential in enhancing ophthalmic public health across various domains. AI offers significant improvements in ophthalmic data management, disease screening and monitoring, risk prediction and early warning systems, medical resource allocation, and health education and patient management. These advancements substantially improve the quality and efficiency of healthcare, particularly in preventing and treating prevalent eye conditions such as cataracts, diabetic retinopathy, glaucoma, and myopia. Additionally, telemedicine and mobile applications have expanded access to healthcare services and enhanced the capabilities of primary healthcare providers. However, there are challenges in integrating AI into ophthalmic public health. Key issues include interoperability with electronic health records (EHR), data security and privacy, data quality and bias, algorithm transparency, and ethical and regulatory frameworks. Heterogeneous data formats and the lack of standardized metadata hinder seamless integration, while privacy risks necessitate advanced techniques such as anonymization. Data biases, stemming from racial or geographic disparities, and the "black box" nature of AI models, limit reliability and clinical trust. Ethical issues, such as ensuring accountability for AI-driven decisions and balancing innovation with patient safety, further complicate implementation. The future of ophthalmic public health lies in overcoming these barriers to fully harness the potential of AI, ensuring that advancements in technology translate into tangible benefits for patients worldwide.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.