AI for Automated Thoracic Disease Assessment from X-Ray Imaging: a Review

With the increasing availability of digital X-ray imaging, artificial intelligence (AI) has emerged as a promising tool for automating the assessment of thoracic diseases. The objective of this study is to systematically review the artificial intelligence (AI) and deep learning methods proposed for the automated assessment of thoracic diseases from chest X-ray images. A thorough search of the relevant literature was conducted, and studies that met the inclusion criteria were critically reviewed. Information on the datasets, model architectures, evaluation metrics, and results was extracted. Convolutional neural networks are prevalent, achieving a state-of-the-art classification performance. Recent studies have explored more complex tasks such as disease localization, segmentation, and report generation. The multitask and multimodal approaches are promising. Challenges related to the data, evaluations, and clinical adoption were identified. This study prevails that there is a significant progress in using deep learning for automated chest X-ray analysis. Further research is needed to validate these models in real-world settings and to facilitate their integration into clinical workflows.