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Purpose This review offers insight into AI’s current and future contributions to medical image analysis. The article highlights the challenges associated with manual image interpretation and introduces AI methodologies, including machine learning and deep learning. It explores AI’s applications in image segmentation, classification, registration, and reconstruction across various modalities like X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Background Medical image analysis is vital in modern healthcare, facilitating disease diagnosis, treatment, and monitoring. Integrating artificial intelligence (AI) techniques, particularly deep learning, has revolutionized this field. Methods Recent advancements are discussed, such as generative adversarial networks (GANs), transfer learning, and federated learning. The review assesses the advantages and limitations of AI in medical image analysis, underscoring the importance of interpretability, robustness, and generalizability in clinical practice. Ethical considerations related to data privacy, bias, and regulatory aspects are also examined. Results The article concludes by exploring future directions, including personalized medicine, multi-modal fusion, real-time analysis, and seamless integration with electronic health records (EHRs). Conclusion This comprehensive review delineates artificial intelligence’s current and prospective role in medical image analysis. With implications for researchers, clinicians, and policymakers, it underscores AI’s transformative potential in enhancing patient care.

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