Current Trends and Future Prospects of Artificial Intelligence in Transforming Radiology - Mahedi, Rezwan Ahmed; Iqbal, Hrishik; Azmee, Raiyan; Azmee, Marzan; Jakir, Fatiha; Nishan, Mufassir Ahmad; Uddin, Mohammed Burhan; Afrin, Sadia (Journal of Current Health Sciences, 2024)
Artificial intelligence (AI) has rapidly transformed numerous industries, including medicine, with radiography standing to benefit significantly from its capabilities. AI enhances diagnostic accuracy, reduces errors, and improves patient care by leveraging large datasets from digital radiographs commonly used in medical and dental practices. Despite these advantages, the impact of AI on image acquisition and radiographer workflows remains underexplored in radiography literature. This review aims to evaluate the effects of AI on radiographic practices, address regulatory challenges, and explore its integration into educational frameworks for radiologists and radiographers. It highlights AI's role in automating tasks, enhancing diagnostic precision, and improving clinical decision-making. A systematic literature search was conducted using PubMed and Google Scholar up to December 2024, with terms including "artificial intelligence," "machine learning," "deep learning," "radiography," and "diagnostic imaging." Seventy-seven peer-reviewed articles and conference papers focusing on AI applications in digital dental radiography were analyzed to extract data on AI methodologies and their potential applications. The findings reveal that AI-powered solutions enhance efficiency in complex imaging tasks, such as lesion identification and triage in mammography, and real-time assessments in cross-sectional imaging, reducing the need for re-scans and increasing patient throughput. However, widespread adoption faces obstacles related to ethical and legal concerns, including data privacy, algorithmic bias, and the need for transparency. While AI demonstrates significant potential to automate workflows, improve diagnostic accuracy, and optimize patient care in radiography, challenges related to human oversight, professional adaptation, and regulatory compliance must be addressed. Further research is needed to fully understand AI’s impact on radiography and to maximize its clinical utility.