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<p>Artificial Intelligence (AI) has emerged as a transformative force in radiology, significantly enhancing the interpretation and review of medical images across modalities such as computed tomography, magnetic resonance imaging, sonography, and X-rays. By leveraging advanced algorithms and machine learning, AI improves diagnostic accuracy, enables early detection of abnormalities, and streamlines workflows, ultimately leading to better patient outcomes and more efficient clinical decision-making. Despite its potential, AI is not without limitations, including the risk of false positives, missed findings, and challenges related to data quality, algorithm interpretability, and regulatory compliance. Furthermore, ethical considerations and the need for ongoing validation and monitoring must be addressed to ensure AI’s reliability and effectiveness. In conclusion, while AI holds immense promise for revolutionizing radiology and healthcare delivery, its successful integration requires careful attention to these challenges to fully harness its potential for improving diagnostic processes and patient care.</p>

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The role of artificial intelligence in the interpretation and review of images across different radiology modalities with a focus on its benefits and challenges

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • Sedighe Hooshmandi,
  • Mohammadjavad Tabatabaii,
  • Hamid Zaferani Arani,
  • Amirreza Heidarian,
  • Sarir Nazemi

摘要

Artificial Intelligence (AI) has emerged as a transformative force in radiology, significantly enhancing the interpretation and review of medical images across modalities such as computed tomography, magnetic resonance imaging, sonography, and X-rays. By leveraging advanced algorithms and machine learning, AI improves diagnostic accuracy, enables early detection of abnormalities, and streamlines workflows, ultimately leading to better patient outcomes and more efficient clinical decision-making. Despite its potential, AI is not without limitations, including the risk of false positives, missed findings, and challenges related to data quality, algorithm interpretability, and regulatory compliance. Furthermore, ethical considerations and the need for ongoing validation and monitoring must be addressed to ensure AI’s reliability and effectiveness. In conclusion, while AI holds immense promise for revolutionizing radiology and healthcare delivery, its successful integration requires careful attention to these challenges to fully harness its potential for improving diagnostic processes and patient care.