The Role of Artificial Intelligence, Machine Learning, and Deep Neural Networks in Medical Imaging: Applications, Strengths, and Challenges

Abstract

The integration of Artificial Intelligence (AI) in medical imaging has revolutionized the field of medical diagnostics, offering unprecedented accuracy and efficiency in disease detection and management. This review paper explores the current role and future potential of AI in medical image diagnosis, summarizing key findings from recent literature. AI techniques, particularly machine learning (ML) and deep learning (DL), have demonstrated remarkable capabilities in analyzing complex medical images, facilitating early detection of diseases, and aiding in clinical decision-making. The reviewed studies highlight AI's success in various medical domains, including oncology, neurology, cardiology, and radiology, where AI has enhanced diagnostic precision and personalized treatment planning. Despite these advancements, challenges such as data heterogeneity, the need for extensive validation, and ethical considerations persist, necessitating further research. This paper underscores the transformative impact of AI in medical imaging and calls for ongoing efforts to overcome existing barriers to fully realize its potential in clinical practice.