The Integration of Big Data, Machine Learning, and Artificial Intelligence in Medical Imaging and Diagnostics
Abstract:
Medical imaging is being transformed by Big Data, Machine Learning (ML), and Artificial Intelligence (AI). These technologies enhance data analytics, predictive modeling, and decision-making. This paper explores their applications, benefits, and challenges in medical imaging. Big Data involves vast amounts of healthcare data requiring advanced processing. ML algorithms, such as convolutional neural networks, analyze image datasets to recognize disease features. AI systems perform tasks like disease diagnosis and treatment recommendations, offering insights that surpass human capabilities. Applications include automating radiologist tasks, enhancing image interpretation, and assisting in procedures like robotic surgery. AI also supports predictive analytics for early disease detection and personalized treatments. Challenges include addressing bias, ensuring data security, and supporting healthcare professionals. Emerging trends involve AI integration with augmented reality (AR), virtual reality (VR), and remote diagnostics. This paper highlights the transformative potential of AI in medical imaging, emphasizing the need for ongoing research, ethical considerations, and collaboration.
