This study presents an innovative approach that leverages large-scale language models (LLMs) to enhance the efficiency of nondestructive testing (NDE) data interpretation for bridge maintenance and safety. We demonstrate the effectiveness of LLMs in interpreting NDE contour maps using various LLMs and providing detailed analysis of bridge conditions. Specifically, nine LLMs were used to interpret five NDE contour maps and evaluated in terms of image description generation, defect identification, actionable recommendations, and accuracy. The results show that four LLMs generate detailed and effective image descriptions, while ChatGPT-4 and Claude 3.5 Sonnet are more effective in generating comprehensive overviews. This study suggests that LLM-based analysis can enhance the efficiency of bridge inspection workflows while maintaining accuracy.