Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation

Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation Zehan LI 1,3, Jinzhi Deng 1,2, Haibing Ma 1,2, Chi Zhang 1, and Dan Xiao 1 1 Moximize.ai 2 Shanghai Zhongqiao Vocational And Technical University 3 China Creative Studies Institute April 22, 2025 Abstract 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. Keywords: Multimodal AI, Industrial Inspection, Translational Framework, TEMAI 1 Introduction Industrial inspection tasks are fundamental to ensuring operational continuity and safety in manufacturing sectors, serving as a cornerstone for preventive maintenance and risk mitigation. These tasks, however, are plagued by systemic inefficiencies, including labor-intensive workflows, hazardous working environments (e.g., high-temperature zones or toxic gas exposure), and heavy reliance on empirical knowledge that is difficult to standardize or transfer across industries[1]. Despite incremental advancements in automation technologies--such as drones, AR-assisted devices, and IoT-enabled sensors--the integration of these tools into inspection workflows has yielded limited returns due to fragmented deployment, high implementation costs, and insufficient interoperability between hardware and software systems [2]. For instance, while drones have reduced human exposure to dangerous environments in power grid inspections, their operational scope remains constrained by battery life and data processing bottlenecks[3].