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Updated: Apr 4, 2026

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AI-Assisted Pneumonia Detection, Localisation and Report Generation from Chest X-rays.

Federico E Boiardi1, Antoine D Lain1, Joram M Posma1

  • 1Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, W12 0NN, United Kingdom.

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This summary is machine-generated.

Large language models (LLMs) improve pneumonia detection in chest X-rays (CXRs) by enhancing data labeling. This deep learning pipeline offers superior diagnostic accuracy and aids in clinical decision-making for pneumonia surveillance.

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computer visioncomputer-aided diagnosisdeep learningmultiple datasetsnatural language processingpneumoniatext generation

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Diagnostic Imaging
  • Machine Learning for Healthcare

Background:

  • Pneumonia detection in chest X-rays (CXRs) faces challenges due to inter-observer variability and overlapping radiographic patterns.
  • Deep learning (DL) solutions show promise but are limited by generalisability and explainability, hindering clinical adoption.
  • Current methods often rely on rule-based natural language processing (rNLP) for report analysis, which can be suboptimal.

Purpose of the Study:

  • To develop and evaluate a holistic deep learning (DL)-based computer-aided diagnosis (CAD) pipeline for pneumonia detection, localisation, and structured report generation from CXRs.
  • To assess the impact of large language model (LLM)-driven relabelling on diagnostic sensitivity compared to traditional rNLP labels.
  • To improve the generalisability and explainability of DL models for pneumonia detection in CXRs.

Main Methods:

  • Curated a large composite dataset of 922,634 publicly available CXRs.
  • Relabelling MIMIC-CXR radiology reports using a local LLM to generate pneumonia labels.
  • Trained DenseNet-121 classifiers on different data configurations (MIMIC-CXR with rNLP and LLM labels, supplemented with VinDr-CXR data).
  • Utilized Gradient-weighted Class Activation Mapping (Grad-CAM) for visual explainability and lung zone-based localisation.

Main Results:

  • LLM-driven relabelling significantly improved human-label agreement (96.5% vs 72.5%, P=1.66×10 -11).
  • The best-performing model (MIMIC-CXR (LLM) + VinDr-CXR) achieved 82.08% sensitivity and 81.97% precision, outperforming radiologist sensitivity ranges and CheXNet.
  • Grad-CAM localisation achieved a moderate F1-score of 52.9%, indicating alignment with pathological regions.

Conclusions:

  • LLM-driven label curation combined with DL surpasses conventional rNLP and radiologist performance in pneumonia detection.
  • The developed CAD pipeline demonstrates potential for rapid triage, automated report drafting, and real-time pneumonia surveillance.
  • This approach advances high-quality data integration in predictive medical imaging, streamlining radiology workflows and mitigating diagnostic errors.