ReviewCurrent opinion in ophthalmology2021
Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice.
Review in Current opinion in ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- The march to harmonized imaging standards for retinal imaging.Progress in retinal and eye research · 2025Review
- Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law.International journal of retina and vitreous · 2025Article
- The application of artificial intelligence-generated content in ophthalmology education.Frontiers in medicine · 2025Review
- AI Horizons in Indian Healthcare: A Vision for Transformation and Equity.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2024Article
- Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11.Ophthalmology science · 2023Article
- Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership (OMOP) Concepts.Ophthalmology science · 2023Article
- Ocular Health and National Data Standards: A Case for Including Visual Acuity in the United States Core Data for Interoperability (USCDI).Ophthalmology science · 2022Article
- Implementation of a Large-Scale Image Curation Workflow Using Deep Learning Framework.Ophthalmology science · 2022Article
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Towards effective data sharing in ophthalmology: data standardization and data privacy.Current opinion in ophthalmology · 2022Review
- Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis.Ophthalmology scienceArticle
- Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.Ophthalmology. GlaucomaReview
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
Abstract
purpose of reviewThe purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, with a specific emphasis on describing gaps in standards development needed for improved integration of artificial intelligence technologies into ophthalmic practice. RECENT
findingsHealthcare standards are an essential component of data exchange and critical for clinical practice, research, and public health surveillance activities. Standards enable interoperability between clinical information systems, healthcare information exchange between institutions, and clinical decision support in a complex health information technology ecosystem. There are several gaps in standards in ophthalmology, including relatively low adoption of imaging standards, lack of use cases for integrating apps providing artificial intelligence -based decision support, lack of common data models to harmonize big data repositories, and no standards regarding interfaces and algorithmic outputs. SUMMARY: These gaps in standards represent opportunities for future work to develop improved data flow between various elements of the digital health ecosystem. This will enable more widespread adoption and integration of artificial intelligence-based tools into clinical practice. Engagement and support from the ophthalmology community for standards development will be important for advancing this work.
Indexed as
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What OpenQuestion 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 OpenQuestion graph.