Digital radiography (DR) represents a transformative advancement in diagnostic imaging, replacing conventional film-screen radiography with detector-based digital acquisition systems. This transition has significantly improved diagnostic efficiency, image quality, and patient safety through reduced radiation exposure and enhanced workflow integration. Digital radiography systems employ flat-panel detectors or computed radiography plates to convert X-ray photons into digital signals, enabling rapid image acquisition and post-processing capabilities. Over the past two decades, digital radiography has become a cornerstone modality across multiple clinical domains, including thoracic imaging, musculoskeletal evaluation, trauma assessment, and neurological screening. The integration of DR with Picture Archiving and Communication Systems (PACS) and Hospital Information Systems (HIS) has facilitated seamless image storage, retrieval, and teleconsultation, thereby enhancing healthcare delivery in both urban and remote settings. Recent literature highlights that digital radiography provides superior contrast resolution and dynamic range compared with conventional radiography, allowing improved visualization of subtle pathological changes. In neuroimaging, DR plays a critical role in initial evaluation of skull fractures, intracranial calcifications, and emergency trauma screening, often serving as a gateway modality prior to advanced imaging such as CT and MRI. Additionally, advancements in artificial intelligence (AI) and machine learning have further augmented the diagnostic potential of digital radiography by enabling automated detection of abnormalities. Despite these advantages, limitations such as high initial infrastructure costs, data storage requirements, and the need for specialized training persist. Furthermore, over-reliance on post-processing may introduce diagnostic pitfalls if not appropriately standardized. This review synthesizes current literature on the principles, technological evolution, clinical applications, advantages, and limitations of digital radiography. It also explores emerging trends, including AI integration and dose optimization strategies. Digital radiography continues to evolve as a fundamental imaging modality, bridging traditional radiography and advanced cross-sectional imaging, and remains integral to modern diagnostic practice.
Ganaie et al. (Wed,) studied this question.