REVOLUTIONIZING MEDICAL IMAGING: THE ROLE OF AI AND DEEP LEARNING IN DIAGNOSIS AND TREATMENT
Keywords:
Artificial Intelligence (AI), Deep Learning, Image Segmentation, Image Classification, Object Detection, Transfer LearningAbstract
The integration of Artificial Intelligence (AI) into medical imaging has revolutionized diagnostic practices, offering the potential for enhanced accuracy, speed, and reduction of errors in clinical decision-making. This article explores the key applications of AI in medical imaging, such as image segmentation, classification, object detection, and image generation, highlighting advanced techniques like U-Net, ResNet, YOLO, and Generative Adversarial Networks (GANs). Despite its transformative potential, AI in medical imaging faces significant challenges, including data privacy concerns, the need for large annotated datasets, model interpretability, and the risk of overfitting. Furthermore, current AI models are limited by potential biases in training data and difficulties in generalizing across diverse populations and imaging modalities. Addressing these challenges is essential to ensure that AI can be effectively and ethically integrated into healthcare, ultimately improving patient outcomes and advancing the field of medical diagnostics.
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