The Impact of AI on Medical Imaging: Advancements in Image Recognition and Diagnostic Accuracy

Abstract

Artificial intelligence systems now used in medical imaging have shifted diagnostic practices by providing advanced technologies that enhance both image analysis and precision of medical assessments. This research examines AI-based deep learning and machine learning approaches that improve medical image accuracy inside clinical practices. A variety of medical conditions can be spotted early by AI algorithms including cancer and fractures and tumours through their analysis of massive medical image volumes by utilizing convolutional neural networks (CNNs). Through its recognition of advanced anomalies AI helps medical professionals make better decisions by lowering diagnosis mistakes while offering expert interpretations. AI system adoption in clinical practice continues to face three main implementation hurdles which include privacy risks related to patient data and complex issues of algorithm clarity and the requirement of superior-quality labelled datasets. Medical imaging relies on Artificial Intelligence to succeed but it demands fundamental infrastructure expansions along with regulatory certifications and substantial employee training investments. Medical imaging applications continue to evolve with AI while simultaneously advancing patient care quality and healthcare delivery speed and cost savings. This evaluation explores the advantages together with difficulties created by AI in medical imaging to present valuable predictions of how diagnostic practices may transform.