Abstract
Artificial intelligence (AI) has rapidly become an important component of medical imaging, offering new opportunities to improve disease detection, diagnosis and clinical decision-making. Medical imaging techniques such as X-ray, computed tomography, magnetic resonance imaging, ultrasound and digital pathology generate large and complex datasets that are increasingly difficult to analyze using conventional methods alone. AI-based approaches, particularly machine learning and deep learning algorithms, have shown strong potential in automating image analysis, enhancing diagnostic accuracy and reducing variability among clinicians.
This review provides a comprehensive overview of the role of artificial intelligence in medical imaging, with a focus on its major applications across radiology, pathology, ophthalmology, cardiovascular imaging, neurological imaging and oncology. In addition, key challenges related to data quality, privacy, model interpretability, algorithmic bias, ethical concerns and integration into clinical workflows are discussed. The review also highlights emerging trends and future perspectives, including federated learning, multimodal imaging analysis, real-time decision support systems and the need for robust regulatory and educational frameworks.
Overall, artificial intelligence has the potential to significantly transform medical imaging and improve patient care when implemented responsibly. Continued research, interdisciplinary collaboration and careful validation are essential to ensure safe, effective and equitable use of AI technologies in clinical practice.