Advancements in Medical Imaging and Diagnostics with MachineLearning: Current Trends and Future Directions

Abstract

Medical imaging has long played a pivotal role in healthcare, from early detection to disease monitoring, making its importance in diagnosis undeniable. Given this critical role, the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) has become essential to enhance diagnostic accuracy and support clinical decision-making. This study focuses on recent advancements in medical imaging and diagnostics following this integration, emphasizing current trends, novel methodologies, clinical applications, and future challenges. Based on an extensive literature search of databases including IEEE Xplore, PubMed, Web of Science, and Scopus from 2015 to 2024, the review highlights the significant utility of supervised and unsupervised learning models, as well as deep learning architectures such as CNNs, U-Net, and Transformers, across diverse fields including radiology, pathology, ophthalmology, cardiology, and dermatology. Innovations such as federated learning and explainable AI are discussed alongside practical challenges including data scarcity, generalizability, and model interpretability. In conclusion, Machine Learning continues to revolutionize medical imaging by offering automated, accurate, and scalable diagnostic solutions. Despite existing limitations, emerging technologies such as quantum computing and edge AI provide a glimpse into the future of personalized and decentralized healthcare. However, widespread clinical adoption requires further research to address critical ethical, regulatory, and data-standardization issues to ensure these powerful tools are implemented effectively and responsibly.

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