AI in Radiology: Transforming Diagnostic Workflow and Accuracy Through Deep Learning
DOI:
https://doi.org/10.63530/IJCSITR_2025_06_03_005Keywords:
Deep learning, AI, radiology, accuracy in diagnosis, convolutional neural networks, medical imagingAbstract
Many changes have been made to how doctors make findings because of AI, especially deep learning (DL) technologies. A type of machine learning called deep learning has greatly improved medical pictures by making diagnosis more accurate, faster, and more scalable. Many types of AI models, mainly convolutional neural networks (CNNs), have been used to look at X-rays, CT scans, MRIs, and ultrasounds. Radiology works in a different way now that this is known. Images can be read automatically, problems can be found, and it's even possible to tell how a sickness will get worse. AI in imaging not only helps doctors make more accurate diagnoses, but it also keeps them from getting tired, so they can work on tougher cases. This essay talks about how AI is being used in imaging, why deep learning is important for medical image analysis, and how it might help improve the accuracy of diagnoses and the speed of the process. It was also stressed the problems and moral issues that come up when AI is used in healthcare, such as the need for government approval, data privacy, and model openness. As AI is used more in healthcare, imaging will become a faster, more accurate, and more personalized way to care for patients.
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Copyright (c) 2025 Hari Suresh Babu Gummadi (Author)
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