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