Natural language processing (NLP) has significant potential within the nuclear industry, yet no prior surveys have focused exclusively on its applications in this sector. Addressing this gap, this review explores recent studies leveraging NLP to enhance key areas, such as equipment reliability, maintenance, compliance, safety, verification, control systems, human-system interfaces, knowledge extraction, and decision-making support in nuclear power plants (NPPs). Our analysis reveals that NLP techniques have successfully automated maintenance recommendations, extracted structured insights from work orders, improved compliance verification, and optimized human-system interactions in NPPs. These advancements have contributed to operational efficiency, cost reduction, and enhanced safety.

This paper also examines the unique challenges of implementing NLP in nuclear settings, including regulatory constraints, data quality issues, domain-specific language complexities, and the integration of large language models (LLMs). To address these challenges, studies have proposed techniques, such as domain-specific dictionaries for handling nuclear terminology, hybrid models combining rule-based and machine learning approaches, and retrieval-augmented generation to improve interpretability and accuracy.

Future directions are proposed, highlighting the importance of real-world testing, model refinement, and the broader adoption of LLMs to improve operational efficiency and safety in NPPs. As the nuclear industry moves toward increased automation, NLP will play a crucial role in bridging the gap between unstructured textual data and actionable intelligence, driving further innovations in safety and decision making.