Decoding AI Integrated Clinical Practice

Abstract

Medical imaging, which provides a non-invasive window into the complex mechanisms of the human body, has completely transformed the healthcare sector. The integration of deep learning techniques into medical image analysis is the primary focus of this work, which describes advances in segmentation, classification, and disease diagnosis tasks. After outlining traditional approaches and their shortcomings, this investigation examines convolutional neural networks (CNNs)—the underlying architecture for most deep learning applications in this discipline. The paper discusses applications across various imaging modalities, including CT, MRI, ultrasound, and X-ray, highlighting real-world achievements, challenges, and the path to clinical adoption. Emphasis is placed on the importance of advanced approaches, such as transfer learning, data augmentation, and explainable AI, in addressing challenges. This exploration also highlights the ethical and regulatory aspects, emphasizing the importance of fairness, transparency, and robustness in clinical applications.

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