ReviewCurrent opinion in ophthalmology2020
Big data requirements for artificial intelligence.
Review in Current opinion in ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Big data in ophthalmology: a systematic review of public databases for ophthalmic research.Eye (London, England) · 2023Pooled it
- Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.World journal of emergency surgery : WJES · 2026Article
- Development and evaluation of an IoT-based hypertension surveillance system in community health settings: a mixed-methods quasi-experimental study protocol.BMC health services research · 2025Article
- Artificial Intelligence Applications in Emergency Toxicology: Advancements and Challenges.Journal of medical Internet research · 2025Review
- Clinical research and trials in echocardiography: rationale, requirements and future opportunities.Echo research and practice · 2025Review
- Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies.Journal of glaucoma · 2025Article
- [Implementation of artificial intelligence (AI) in healthcare: historical development, current technologies and challenges].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- New implementation of data standards for AI in oncology: Experience from the EuCanImage project.GigaScience · 2025Article
- Developing a 10-Layer Retinal Segmentation for MacTel Using Semi-Supervised Learning.Translational vision science & technology · 2024Article
- The role of registries in improving health and bridging healthcare, research, education, innovation and development: a research department perspective.The Journal of international medical research · 2024Review
- 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
- Enhancing single-cell biology through advanced AI-powered microfluidics.Biomicrofluidics · 2023Article
- Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology.Journal of clinical medicine · 2023Review
- Visual Acuity: Assessment of Data Quality and Usability in an Electronic Health Record System.Ophthalmology science · 2023Article
- Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations.Ophthalmology · 2023Article
- Big data in corneal diseases and cataract: Current applications and future directions.Frontiers in big data · 2023Review
- Research on artificial intelligence in shoulder and elbow surgery is increasing.JSES international · 2023Article
- Towards standardizing retinal optical coherence tomography angiography: a review.Light, science & applications · 2022Review
- Change Management and Digital Innovations in Hospitals of Five European Countries.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purpose of reviewTo summarize how big data and artificial intelligence technologies have evolved, their current state, and next steps to enable future generations of artificial intelligence for ophthalmology. RECENT
findingsBig data in health care is ever increasing in volume and variety, enabled by the widespread adoption of electronic health records (EHRs) and standards for health data information exchange, such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources. Simultaneously, the development of powerful cloud-based storage and computing architectures supports a fertile environment for big data and artificial intelligence in health care. The high volume and velocity of imaging and structured data in ophthalmology and is one of the reasons why ophthalmology is at the forefront of artificial intelligence research. Still needed are consensus labeling conventions for performing supervised learning on big data, promotion of data sharing and reuse, standards for sharing artificial intelligence model architectures, and access to artificial intelligence models through open application program interfaces (APIs). SUMMARY: Future requirements for big data and artificial intelligence include fostering reproducible science, continuing open innovation, and supporting the clinical use of artificial intelligence by promoting standards for data labels, data sharing, artificial intelligence model architecture sharing, and accessible code and APIs.
Indexed as
Identifiers
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.