The Integration of Big Data, Machine Learning, and Artificial Intelligence in Medical Imaging and Diagnostics

Medical imaging is being transformed by Big Data, Machine Learning (ML), and ArtificialIntelligence (AI). These technologies enhance data analytics, predictive modeling, anddecision-making. This paper explores their applications, benefits, and challenges in medicalimaging.Big Data involves vast amounts of healthcare data requiring advanced processing. MLalgorithms, such as convolutional neural networks, analyze image datasets to recognizedisease features. AI systems perform tasks like disease diagnosis and treatmentrecommendations, offering insights that surpass human capabilities.Applications include automating radiologist tasks, enhancing image interpretation, andassisting in procedures like robotic surgery. AI also supports predictive analytics for earlydisease detection and personalized treatments.Challenges include addressing bias, ensuring data security, and supporting healthcareprofessionals. Emerging trends involve AI integration with augmented reality (AR), virtualreality (VR), and remote diagnostics.This paper highlights the transformative potential of AI in medical imaging, emphasizingthe need for ongoing research, ethical considerations, and collaboration.