用实施科学加速AI医学影像从研发到临床落地
What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging
- 引入实施科学框架系统解决AI医疗影像落地难题
- 实证显示技术从研究到应用平均延迟17年
- 适合关注AI临床转化的研究者与医疗机构
人工智能在医学影像领域的变革潜力已被广泛认可，但多数工具在真实临床环境中难以推广。事实上，技术从证据生成到实际应用平均存在17年的延迟。实施科学（IS）提供了一种基于证据的实践框架，可通过系统性方法、策略及混合研究设计，缩短这一差距。本文指出医学影像工作流中AI采纳面临基础设施、教育与文化等多重障碍，并强调有效性研究与实施研究的互补作用，提倡采用混合研究设计、整合知识转化（iKT）、利益相关方参与及公平导向的共同创造，以设计可持续且可推广的解决方案。同时探讨将人机交互（HCI）框架融入医学影像，提升AI可用性。采用实施科学不仅是方法论进步，更是加速创新转化为患者获益的战略需要。
原文摘要 · Abstract (English)
The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use, to shorten this lag through systematic frameworks, strategies, and hybrid research designs. We outline challenges specific to AI adoption in MI workflows, including infrastructural, educational, and cultural barriers. We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs and the role of integrated KT (iKT), stakeholder engagement, and equity-focused co-creation in designing sustainable and generalizable solutions. We discuss integration of Human-Computer Interaction (HCI) frameworks in MI towards usable AI. Adopting IS is not only a methodological advancement; it is a strategic imperative for accelerating translation of innovation into improved patient outcomes.
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