ArticleTheranostics2019
Artificial intelligence-based decision-making for age-related macular degeneration.
Article in Theranostics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 4 of them syntheses 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
75 citing papers in PubMed, 4 syntheses or guidelines pooled it, 185 citations in OpenAlex.
- Artificial intelligence and patient reported outcomes in ophthalmology.Journal of patient-reported outcomes · 2026Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis.Eye (London, England) · 2022Pooled it
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.BMJ (Clinical research ed.) · 2020Pooled it
- A comparison of GPT-4V's capability in optical coherence tomography images of age-related macular degeneration with expert assessments.BMC ophthalmology · 2026Article
- Artificial intelligence based prediction of first recurrence in neovascular age related macular degeneration with validation by 19 experts.Scientific reports · 2026Article
- Real-world evaluation of an OCT-based AI decision-support system for neovascular AMD activity triage in teleophthalmology.Frontiers in ophthalmology · 2026Article
- AI-based fluid quantification in neovascular age-related macular degeneration and diabetic macular edema treated with faricimab - a real-life study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- [Research on attention-enhanced networks for subtype classification of age-related macular degeneration in optical coherence tomography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence.Scientific reports · 2025Article
- Artificial Intelligence and Assistive Robotics in Healthcare Services: Applications in Silver Care.International journal of environmental research and public health · 2025Review
- ChatOCT: Embedded Clinical Decision Support Systems for Optical Coherence Tomography in Offline and Resource-Limited Settings.Journal of medical systems · 2025Article
- Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.Biophysical journal · 2025Article
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.Journal of medical signals and sensors · 2025Article
- A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer.Diagnostics (Basel, Switzerland) · 2024Article
- Imaging the eye as a window to brain health: frontier approaches and future directions.Journal of neuroinflammation · 2024Review
- Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) based on convolutional neural networks (CNNs) has a great potential to enhance medical workflow and improve health care quality. Of particular interest is practical implementation of such AI-based software as a cloud-based tool aimed for telemedicine, the practice of providing medical care from a distance using electronic interfaces.
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.