ReviewFrontiers in artificial intelligence2021
Deep Learning and its Application for Healthcare Delivery in Low and Middle Income Countries.
Review in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image-Based Deep Learning for Cataract Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- AI-driven dual-task prediction model for co-stratifying efficacy and toxicity in NSCLC immunotherapy.PLOS digital health · 2026Article
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- Radiomics analysis for the early diagnosis of common sexually transmitted infections and skin lesions.PLOS digital health · 2025Article
- The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence.Molecular biotechnology · 2025Review
- Perspectives of Digital Health Innovations in Low- and Middle-Income Health Care Systems From South and Southeast Asia.Journal of medical Internet research · 2024Article
- Artificial intelligence in respiratory care: perspectives on critical opportunities and challenges.Breathe (Sheffield, England) · 2024Review
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Optical imaging technologies forCurrent opinion in biomedical engineering · 2023Article
- Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals.Acta radiologica open · 2023Article
- Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.Journal of the Association for Research in Otolaryngology : JARO · 2022Review
- Optimizing human-centered AI for healthcare in the Global South.Patterns (New York, N.Y.) · 2022Review
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
No grant is acknowledged in the PubMed record.
Abstract
As anyone who has witnessed firsthand knows, healthcare delivery in low-resource settings is fundamentally different from more affluent settings. Artificial Intelligence, including Machine Learning and more specifically Deep Learning, has made amazing advances over the past decade. Significant resources are now dedicated to problems in the field of medicine, but with the potential to further the digital divide by neglecting underserved areas and their specific context. In the general case, Deep Learning remains a complex technology requiring deep technical expertise. This paper explores advances within the narrower field of deep learning image analysis that reduces barriers to adoption and allows individuals with less specialized software skills to effectively employ these techniques. This enables a next wave of innovation, driven largely by problem domain expertise and the creative application of this technology to unaddressed concerns in LMIC settings. The paper also explores the central role of NGOs in problem identification, data acquisition and curation, and integration of new technologies into healthcare systems.
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.