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

arXiv: https://arxiv.org/abs/2504.13873 · HTML: https://mlsanity.com/paper/2504.13873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning](https://mlsanity.com/paper/2401.06805/) (2024)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4](https://mlsanity.com/paper/2402.12782/) (2024)
- [Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology](https://mlsanity.com/paper/2003.09802/) (2020)

## Built on by (most cited first, 1 of 1)

- [From Horizontal Layering to Vertical Integration: A Comparative Study of the AI-Driven Software Development Paradigm](https://mlsanity.com/paper/2601.22667/) (2026)

## Abstract (arXiv)

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.