ArticleScientific reports2024
Deep learning improves physician accuracy in the comprehensive detection of abnormalities on chest X-rays.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identifying the diagnostic utility of artificial intelligence for elbow effusion detection: A systematic review and meta-analysis.Emergency radiology · 2026Pooled it
- Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Degenerative cervical myelopathy diagnosis: applicability of artificial intelligence-enhanced modalities in a systematic review and meta-analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- FUO-PETMamba: a pet maximum-intensity-projection-based artificial intelligence framework for aetiological classification of fever of unknown origin with multicentre validation.European journal of nuclear medicine and molecular imaging · 2026Article
- Diagnostic performance with sensitivity/specificity optimized computer-aided detection to detect common abnormalities in chest radiography.Japanese journal of radiology · 2026Article
- Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.PLOS digital health · 2026Review
- Can AI substitute the first reader in chest radiograph screening? A retrospective non-inferiority evaluation.Japanese journal of radiology · 2026Article
- Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.BMC medical education · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Clinical artificial intelligence applications of vision-language foundation models.PLOS digital health · 2026Review
- A Pilot Study Protocol for AI-Assisted Interpretation of Chest X-rays for Pulmonary Abnormalities in Uganda.Cureus · 2026Article
- Bridging the expertise gap: how AI-assisted stroke detection levels the playing field in emergency medicine.European radiology · 2026Article
- Externally Validated Deep Learning Analysis of Chest Radiographs for Differentiating COVID-19 and Viral Pneumonia.Diagnostics (Basel, Switzerland) · 2026Article
- Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.BMC medical informatics and decision making · 2026Article
- MCADS: Simultaneous Detection and Analysis of 18 Chest Radiographic Abnormalities Using Multi-Label Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- Artificial intelligence (AI)-driven ensemble model for comprehensive chest X-ray abnormality detection and deployment.The Indian journal of medical research · 2026Article
- Review
- MultiModal craniocerebral diagnose based on 3D CT and image reports.Scientific reports · 2025Article
Corrections and comments
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11 authors.
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Abstract
Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system. The AI system accurately detected chest X-ray abnormalities (AUC: 0.976, 95% bootstrap CI: 0.975, 0.976) and generalized to a publicly available dataset (AUC: 0.975, 95% bootstrap CI: 0.971, 0.978). Physicians showed significant improvements in detecting abnormalities on chest X-rays when aided by the AI system compared to when unaided (difference in AUC: 0.101, p < 0.001). Non-radiologist physicians detected abnormalities on chest X-ray exams as accurately as radiologists when aided by the AI system and were faster at evaluating chest X-rays when aided compared to unaided. Together, these results show that the AI system is accurate and reduces physician errors in chest X-ray evaluation, which highlights the potential of AI systems to improve access to fast, high-quality radiograph interpretation.
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