Advances in Radiological Imaging Modalities and Their Expanding Role in the Early Diagnosis, Monitoring, and Prognosis of Internal Medicine Disorders: A Comprehensive Review

This review explores the growing role of advanced radiological imaging in internal medicine, focusing on its applications in prognosis prediction, disease monitoring, and early diagnosis. It highlights how developments in computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and the integration of artificial intelligence (AI) are reshaping clinical decision-making in fields such as neurology, cardiology, and oncology. Although progress has been substantial, widespread adoption is still limited by high costs, unequal access, and the absence of consistent protocols. The use of AI in combination with radiomics, the quantitative study of medical images, has enhanced diagnostic accuracy and expanded opportunities for outcome prediction and treatment planning. However, challenges remain, including inconsistencies in data quality, regulatory barriers, and the pressing need for validation through large-scale, multicenter studies. Hybrid technologies such as PET/MRI, which combine functional and anatomical imaging, hold particular promise for improving diagnostic precision in neurology and oncology. Together, these innovations illustrate the transformative potential of modern imaging to enable earlier interventions and support more personalized care strategies. The review emphasizes the need for validation, standardized frameworks, and international collaboration to overcome current limitations. Addressing these concerns will broaden the accessibility of these tools, fostering more equitable healthcare and improved outcomes worldwide. Finally, it provides practical guidance for clinicians, researchers, and policymakers who must adapt to the rapidly advancing landscape of radiological imaging.

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Publication Details

Journal
Cureus
Published
2025-12-18
DOI
https://doi.org/10.7759/cureus.99513
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Advances in Radiological Imaging Modalities and Their Expanding Role in the Early Diagnosis, Monitoring, and Prognosis of Internal Medicine Disorders: A Comprehensive Review

Cureus
Radiomics and Machine Learning in Medical Imaging
article

Advances in Radiological Imaging Modalities and Their Expanding Role in the Early Diagnosis, Monitoring, and Prognosis of Internal Medicine Disorders: A Comprehensive Review

article en

Abstract

This review explores the growing role of advanced radiological imaging in internal medicine, focusing on its applications in prognosis prediction, disease monitoring, and early diagnosis. It highlights how developments in computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, and the integration of artificial intelligence (AI) are reshaping clinical decision-making in fields such as neurology, cardiology, and oncology. Although progress has been substantial, widespread adoption is still limited by high costs, unequal access, and the absence of consistent protocols. The use of AI in combination with radiomics, the quantitative study of medical images, has enhanced diagnostic accuracy and expanded opportunities for outcome prediction and treatment planning. However, challenges remain, including inconsistencies in data quality, regulatory barriers, and the pressing need for validation through large-scale, multicenter studies. Hybrid technologies such as PET/MRI, which combine functional and anatomical imaging, hold particular promise for improving diagnostic precision in neurology and oncology. Together, these innovations illustrate the transformative potential of modern imaging to enable earlier interventions and support more personalized care strategies. The review emphasizes the need for validation, standardized frameworks, and international collaboration to overcome current limitations. Addressing these concerns will broaden the accessibility of these tools, fostering more equitable healthcare and improved outcomes worldwide. Finally, it provides practical guidance for clinicians, researchers, and policymakers who must adapt to the rapidly advancing landscape of radiological imaging.

CureusVol. 17(12)
JSS Academy of Higher Education and Research (IN), SRM Institute of Science and Technology (IN), Sanjay Gandhi Post Graduate Institute of Medical Sciences (IN), Christian Medical College, Vellore (IN), Christian Medical College (IN), Patanjali Research Foundation (IN), Ospedale Maggiore di Lodi (IT), JSS Dental College and Hospital (IN)
Openalex Percentile: Top 32%
Radiomics and Machine Learning in Medical Imaging
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