ArticleBMC ophthalmology2021
Generalisability through local validation: overcoming barriers due to data disparity in healthcare.
Article in BMC ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed.
- A systematic review of prolonged posture monitoring with accelerometer-based wearable sensors.PLOS digital health · 2026Article
- The Algorithm's Blindspot: An Ethical Critique of Data-Fetishism and the Deskilling of the Clinical Gaze in Ophthalmology.Journal of bioethical inquiry · 2026Article
- "Overfitting or insight?" Re-evaluating a blood-based machine learning model for high myopia.Eye (London, England) · 2026Article
- A validated model for early prediction of group A streptococcal aetiology in necrotising soft tissue infections using minimal patient data.BMC medicine · 2026Article
- Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.Translational vision science & technology · 2025Article
- Prediction of Myopia Among Undergraduate Students by Using Ensemble Machine Learning Techniques.Health science reports · 2025Article
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- Studies of Artificial Intelligence/Machine Learning Registered on ClinicalTrials.gov: Cross-Sectional Study With Temporal Trends, 2010-2023.Journal of medical Internet research · 2024Article
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Real-World Evidence Studies in Oncology Therapeutics: Hope or Hype?Indian journal of surgical oncology · 2023Review
- Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.BMJ open ophthalmology · 2023Review
- Digital Education for the Deployment of Artificial Intelligence in Health Care.Journal of medical Internet research · 2023Article
- Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium.Mayo Clinic proceedings. Digital health · 2023Review
- Sociodemographic disparities in ophthalmological clinical trials.BMJ open ophthalmology · 2023Review
- Global disparity bias in ophthalmology artificial intelligence applications.BMJ health & care informatics · 2022Article
- Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review.PLOS digital health · 2022Article
- Optimizing human-centered AI for healthcare in the Global South.Patterns (New York, N.Y.) · 2022Review
- Best practices in the real-world data life cycle.PLOS digital health · 2022Review
- Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices.Journal of medical Internet research · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.The importance of external ML model validation on diverse populations should be highlighted where possible - especially for models built with single-centre data.
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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.