ArticleNPJ digital medicine2020
CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 6 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 6 syntheses or guidelines pooled it, 134 citations in OpenAlex.
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- eHealth in TB clinical management.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022Pooled it
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph.Frontiers in medicine · 2022Pooled it
- Academia and industry in the age of artificial intelligence: a narrative review.BMJ digital health & AI · 2026Review
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT.Healthcare informatics research · 2025Article
- Understanding Providers' Attitude Toward AI in India's Informal Health Care Sector: Survey Study.JMIR formative research · 2025Article
- Applications and prospects of artificial intelligence in the auxiliary diagnosis of pediatric pulmonary tuberculosis.Frontiers in microbiology · 2025Review
- Denoising diffusion model for increased performance of detecting structural heart disease.medRxiv : the preprint server for health sciences · 2024Article
- Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.Scientific reports · 2024Article
- Three Challenges for AI-Assisted Decision-Making.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- For Tuberculosis, Not "To Screen or Not to Screen?" but "Who?" and "How?"Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.Journal of imaging informatics in medicine · 2024Article
- Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.MedComm · 2024Article
- Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.PloS one · 2024Article
- Human-machine cooperation meta-model for clinical diagnosis by adaptation to human expert's diagnostic characteristics.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (
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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.