The Evolving Landscape of Artificial Intelligence in Biomedical Imaging

Abstract

Artificial intelligence (AI) is transforming biomedical imaging and diagnostics by enabling faster, more accurate, and personalized healthcare solutions. The rise of smart digital ecosystems—integrating cloud computing, the Internet of Things (IoT), and digital twins—has further accelerated the clinical adoption of AI technologies. This study presents a comprehensive bibliometric analysis of AI applications in biomedical imaging and diagnostics from 2019 to 2025, using data retrieved from the Web of Science (WoS) database. Through VOSviewer-Based mapping of 893 publications, key research trends, influential contributors, and international collaboration networks are identified. Dominant research themes include deep learning for medical image segmentation, generative models for synthetic data augmentation, federated learning for privacy-preserving healthcare, and explainable AI (XAI) for enhancing clinical transparency. Temporal evolution analysis reveals a shift from convolutional neural networks (CNNs) toward transformer and diffusion models in recent years. The findings emphasize the field’s increasingly interdisciplinary nature and highlight the need for diverse datasets, ethical frameworks, and interpretable models to ensure trustworthy and scalable AI integration in healthcare. This study provides a systematic and data-driven foundation to guide future research, collaboration, and policy development in the advancement of AI-enabled biomedical imaging.

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