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<p>Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the “black box” nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway—uniting computer scientists, clinicians, and regulatory bodies—required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.</p>

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From intelligent models to clinical tools: the evolving landscape of AI in medical imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月2日

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  • Dmitrii Kaplun,
  • Markus Graf,
  • Haifeng Wang

摘要

Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the “black box” nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway—uniting computer scientists, clinicians, and regulatory bodies—required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.