发表机构
BC Cancer Research Institute; University of British Columbia; Vector Institute; Institute of Nuclear Medicine（BC癌症研究所; 不列颠哥伦比亚大学; 向量研究所; 核医学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文指出医学影像AI研究转化不足源于结构性错位，明确六大错位维度并提出重构路径，最终愿景是开发与医生对齐、扩展而非替代临床判断的智能体AI。
AI 中文摘要
医学影像是临床人工智能（AI）的主要试验场，但十年的密集研究并未转化为相应的床边影响。我们认为，这一差距主要并非源于算法性能不足、监管不足或可解释性有限，而是反映了AI系统的设计与评估方式和临床决策方式之间的结构性错位。本文视角明确了这种错位的六个相互关联的维度：在多模态临床世界中仅像素模型的主导地位；通过不透明且僵化的系统侵蚀医生信任；基础模型在数据稀疏的医学领域未兑现承诺；不可共享、整理不足的数据集持续存在瓶颈；已验证算法与可部署临床平台之间的差距；以预测为中心的AI未能生成可操作的临床指导。针对每个维度，我们重构了问题并提出了前进路径，最终形成了智能体、与医生对齐的AI愿景，该AI扩展而非替代临床判断。
英文摘要
Medical imaging has served as primary proving ground for clinical artificial intelligence (AI), yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or limited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This Perspective identifies six interconnected dimensions of this misalignment: the dominance of pixel-only models in a multimodal clinical world; the erosion of physician trust through opaque and inflexible systems; the unfulfilled promise of foundation models in data-sparse medical domains; the persistent bottleneck of non-shareable, under-curated datasets; the gap between validated algorithms and deployable clinical platforms; and the failure of prediction-centric AI to generate actionable clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of agentic, physician-aligned AI that extends, rather than replaces, clinical judgment.
Comments10 pages, 2 figures
