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<p>This paper presents a critical analysis of AI advancements in chest radiograph (CXR) analysis, tracing its evolution from conventional machine learning to deep learning and multimodal approaches. Early models relied on hand-crafted features, while recent CNNs and transformer-based architectures now achieve diagnostic accuracies exceeding or comparable to radiologists for various thoracic conditions. The recent integration of large language models and multimodal systems—combining imaging with clinical text—has further improved performance and interpretability. Despite notable success, challenges still remain, including model bias, limited generalisation across institutions, and explainability. Solutions such as data sharing, domain adaptation, and explainable-AI techniques are actively being explored. Looking forward, AI systems trained on diverse patient data streams promise enhanced clinical integration and diagnostic precision.</p>

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Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Hidetoshi Matsuo,
  • Mizuho Nishio,
  • Koji Fujimoto,
  • Nicolas Deperrois,
  • Takaaki Matsunaga,
  • Farhad Nooralahzadeh,
  • Michael Krauthammer,
  • Takamichi Murakami

摘要

This paper presents a critical analysis of AI advancements in chest radiograph (CXR) analysis, tracing its evolution from conventional machine learning to deep learning and multimodal approaches. Early models relied on hand-crafted features, while recent CNNs and transformer-based architectures now achieve diagnostic accuracies exceeding or comparable to radiologists for various thoracic conditions. The recent integration of large language models and multimodal systems—combining imaging with clinical text—has further improved performance and interpretability. Despite notable success, challenges still remain, including model bias, limited generalisation across institutions, and explainability. Solutions such as data sharing, domain adaptation, and explainable-AI techniques are actively being explored. Looking forward, AI systems trained on diverse patient data streams promise enhanced clinical integration and diagnostic precision.