AI-BASED IMAGE ANALYSIS FOR IMPROVED ACCURACY IN RADIOLOGY AND MEDICAL IMAGING
Author(s): VEERAVARAPRASAD PINDI
Publication #: 2407069
Date of Publication: 09.01.2019
Country: India
Pages: 1-11
Published In: Volume 5 Issue 1 January-2019
DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805371
Abstract
Artificial intelligence (AI) has revolutionized medical imaging by significantly enhancing diagnostic accuracy and workflow efficiency. This survey paper comprehensively examines AI's integration into radiology and medical imaging, starting with an overview of its pivotal role in healthcare. Key AI concepts such as machine learning, deep learning, and neural networks are elucidated, alongside a detailed exploration of algorithms like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) that underpin AI applications in image analysis. The paper reviews AI's impact on diagnostic imaging modalities such as X-ray, MRI, and CT scans, emphasizing image segmentation and computer-aided diagnosis (CAD) systems. Challenges including data quality, interpretability of AI-driven diagnoses, and regulatory and ethical considerations are discussed. Case studies illustrate successful AI implementations in clinical settings, highlighting improvements in diagnostic accuracy and operational efficiency. Looking forward, the paper explores future trends in AI techniques, predicting advancements that could further optimize personalized medicine and clinical decision support systems in radiology and medical imaging, reaffirming AI's transformative potential in healthcare.
Keywords: Radiology AI, Medical Imaging, AI Algorithms, Diagnostic accuracy
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