Publication:

Artificial intelligence in medical imaging: bridging innovation, ethics, and clinical impact

Medical imaging is essential for diagnosis, treatment planning, and disease monitoring. The integration of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is revolutionizing this field by automating image analysis and improving diagnostic performance. This review synthesizes recent advancements in AI applications for medical imaging, with a focus on radiology, oncology, and digital pathology. Core methodologies, including image classification, segmentation, reconstruction, and multimodal integration, are examined alongside emerging approaches such as federated learning and explainable AI. AI models demonstrate strong potential in enhancing diagnostic accuracy, reducing variability, and improving workflow efficiency. However, key barriers remain, including data quality limitations, algorithmic bias, lack of interpretability, and regulatory challenges. Novel strategies, including cross-modality fusion and privacy-preserving frameworks, are being explored to address these issues and improve generalizability. AI-driven medical imaging tools are poised to advance personalized care and clinical decision-making. Achieving widespread adoption will require fairness, transparency, clinician engagement, and rigorous real-world validation to ensure safe and effective integration into healthcare practice.