Navigating the AI Landscape in Medical Imaging: A Critical Analysis of Technologies, Implementation, and Implications

Abstract

The growing volume and complexity of medical imaging outpaces the available radiologist workforce, risking timely diagnosis. Comprehensive artificial intelligence (AI) that integrates multimodal imaging data, clinical notes, and large language models has the potential to support radiologists. Accordingly, the U.S. Food and Drug Administration has cleared more than 770 AI medical devices that focus on radiology, primarily based on deep learning. However, algorithm development and validation remain challenging. Limitations include sparse expert-annotated data and regulatory hurdles. Clinical implementation and the adaptation of the radiologic community is also lagging behind. Additionally, technical barriers exist regarding data availability, large language model explainability, deep learning model generalization, and clinical integration. Advances in few-shot learning, self-supervised models, and centralized platforms may support consolidated AI ecosystems. Although progress has been made, much work is still needed on data infrastructure, responsible clinical translation, and workflow integration. Continuous multidisciplinary efforts are required to optimize AI safety and truly augment radiologists' work through comprehensive solutions. By overcoming the remaining challenges, AI may strengthen health care systems through improved diagnosis. This review addresses integration challenges, pathways for responsible progress, and the viewpoints of all stakeholders.

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