Advancements in Imaging Modalities: Exploring the Potential of Artificial Intelligence in Radiology

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Dr Farah kalsoom Dr Shalmeen Tariq Komal mushtaq Umaima zafar Enas Haridy ahmed


Artificial Intelligence, Radiology, Imaging Modalities, Diagnostic Accuracy, Workflow Efficiency.


Over the past decade, the integration of artificial intelligence (AI) into radiology has shown promise in enhancing diagnostic accuracy, efficiency, and patient outcomes. With the rapid advancements in imaging modalities, the potential of AI to revolutionize radiological practice has garnered significant attention.

Aim: This study aimed to investigate the efficacy of integrating AI algorithms into various imaging modalities within radiology to improve diagnostic accuracy and streamline workflow processes.

Methods: A comprehensive review of literature was conducted to identify studies and developments pertaining to the integration of AI in radiology. Additionally, a six-month prospective study was undertaken involving a cohort of 100 patients undergoing various imaging procedures, where AI algorithms were utilized alongside conventional radiological interpretation methods.

Results: The integration of AI algorithms into radiological practice demonstrated significant improvements in diagnostic accuracy across multiple imaging modalities. AI-assisted interpretations exhibited enhanced sensitivity and specificity compared to conventional methods. Moreover, the incorporation of AI facilitated the automation of routine tasks, thereby optimizing workflow efficiency.

Conclusion: Our findings underscore the potential of AI in augmenting radiological practice by improving diagnostic accuracy and workflow efficiency. The successful integration of AI algorithms into various imaging modalities holds promise for enhancing patient care and outcomes in radiology.

Abstract 34 | PDF Downloads 17


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