Inteligência artificial na radiologia: aplicações e impactos na ressonância magnética e tomografia computadorizada

Abstract

Introduction: Technological advancements are always closely linked to radiology, and the trajectory of Artificial Intelligence (AI) in radiology reflects significant progress in computing, machine learning, and the development of medical imaging algorithms. As a cornerstone of medicine, radiology has benefited from AI, particularly in image interpretation and diagnostics, allowing for optimized disease detection, greater accuracy in anatomical segmentation, and reduced analysis time. However, the application of AI faces challenges, including regulatory issues and the need for adequate technological infrastructure, especially in developing countries like Brazil. Methods: This is a narrative literature review aimed at identifying the key advancements and challenges in the application of AI in radiology. Results: AI has proven effective in enhancing diagnostic accuracy and operational efficiency in radiology. Notable advancements include algorithms capable of automatically segmenting complex anatomical structures and detecting lesions, such as tumors and pulmonary anomalies, in their early stages. The automation of repetitive tasks for radiologists, enabled by AI, has contributed to optimizing workflow, reducing analysis time, and increasing throughput. However, the full implementation of AI still faces challenges, such as insufficient regulation and the lack of advanced infrastructure, limiting its widespread use, particularly in Brazil. Conclusion: AI has the potential to transform radiological practice by enabling faster and more accurate diagnoses and optimizing clinical process efficiency. To realize its full potential, investments in infrastructure, regulatory policies, and professional training are necessary. Future prospects suggest that AI will be able to personalize diagnoses and expand its use in both public and private hospitals, significantly improving patient care and access to high-quality diagnostics.