提出工业质检中多模态AI落地评估框架，解决技术好但用不起来的难题。
Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation
- 从医疗转化研究借鉴思路，构建能力、采纳、价值三维度评估体系。
- 实证显示相同技术下降率下，不同行业价值实现差异显著。
- 提供可落地的路径与系数指标，适合制造企业部署AI质检系统。
本文提出多模态工业质检AI转化评估框架（TEMAI），将医疗领域的转化研究理念引入工业场景。框架包含三大核心维度：技术可行性（Capability）、组织接纳度（Adoption）与价值实现（Utility）。研究表明，仅具备技术能力无法带来实际价值，必须匹配相应的采纳机制。TEMAI引入价值密度系数等专用指标，并设计结构化实施路径。在零售与光伏质检场景的实证验证中，尽管技术能力下降率相似，但价值实现模式存在显著差异，证明框架在多行业中的有效性，同时强调需采用行业定制化适配策略。
原文摘要 · Abstract (English)
This paper introduces the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework, bridging multimodal AI capabilities with industrial inspection implementation. Adapting translational research principles from healthcare to industrial contexts, TEMAI establishes three core dimensions: Capability (technical feasibility), Adoption (organizational readiness), and Utility (value realization). The framework demonstrates that technical capability alone yields limited value without corresponding adoption mechanisms. TEMAI incorporates specialized metrics including the Value Density Coefficient and structured implementation pathways. Empirical validation through retail and photovoltaic inspection implementations revealed significant differences in value realization patterns despite similar capability reduction rates, confirming the framework's effectiveness across diverse industrial sectors while highlighting the importance of industry-specific adaptation strategies.
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