# Large Language Models' Trustworthiness in the Light of the EU AI Act--A Systematic Mapping Study
## 5 Results and Discussion
### Results of RQ2  
**Research Question:** What are the current research gaps in the study of LLM trustworthiness in popular application domains? **Objective:** Identify areas in LLM trustworthiness research that need further exploration, especially within high-impact application domains. Analysis and Results  
Figure 6 illustrates the general distribution of studies exploring LLM trustworthiness across various application domains based on selected primary studies. The analysis reveals several key insights into the current state of research and highlights potential gaps:  
* **Linguistic Applications:** This domain accounts for the largest share of studies, with 496 papers (17.20%). This reflects the significant interest in improving language comprehension, generation, and translation tasks in LLMs. However, the focus on linguistic applications may overshadow trustworthiness considerations in other high-impact domains. * **Healthcare:** With 464 papers (16.09%), healthcare is the second-most explored domain, demonstrating the growing reliance on LLMs in medical decision-making, diagnostics, and patient interaction. Despite this focus, challenges in data governance, robustness, and transparency remain underexplored. * **Education:** Trustworthiness in educational applications has been explored in 311 papers (10.78%). The increasing use of LLMs in personalized learning and content creation raises concerns about accuracy, bias, and human oversight. * **Information Verification:** This domain has 282 papers (9.78%), reflecting the growing importance of LLMs in verifying information, particularly in the context of news, media, and social media content. Despite its relevance, further research into transparency and robustness in this area is necessary. * **Smart System/Coding:** This domain has 217 papers (7.52%), showcasing the role of LLMs in software development and automation. Trustworthiness in coding tasks requires ensuring reliability, security, and the ability to handle ambiguous situations. * **Underexplored Domains:** Several critical domains such as _cybersecurity_ (53 papers, 1.84%), _finance_ (51 papers, 1.77%), and _environment_ (15 papers, 0.52%) have received  
Figure 5: Trustworthiness aspects considered in LLM research. limited attention. These domains involve high stakes, where trustworthy AI is essential, highlighting a significant research gap. * **Emerging and Niche Domains:** Application areas such as _smart factory_ (13 papers, 0.45%), _robotics_ (11 papers, 0.38%), _Internet of Things (IoT)_ (9 papers, 0.31%), _automotive_ (9 papers, 0.31%), _surveillance_ (7 papers, 0.24%), _air space_ (5 papers, 0.17%), _construction_ (3 papers, 0.10%), _defense_ (2 papers, 0.07%), _agriculture_ (2 papers, 0.07%), _telecommunication_ (1 paper, 0.03%), and _marine_ (1 paper, 0.03%) remain largely unexplored. These domains demand robust and transparent LLMs due to their direct impact on safety, security, and privacy. * **General Observations:** A significant portion of studies (268 papers, 9.29%) did not specify a domain, indicating a lack of targeted research. Furthermore, domains like _smart business_ (91 papers, 3.16%) and _social scoring_ (150 papers, 5.20%) also demonstrate a notable gap in research focused on trustworthiness. These findings suggest that while certain domains such as _linguistics_, _healthcare_, and _education_ have seen substantial research, there is an urgent need to extend trustworthiness studies to underexplored and high-stakes application areas. Addressing these gaps will enhance the applicability of LLMs across diverse fields while ensuring their responsible and reliable use. Furthermore, Figure 7 provides a closer look into the trends in the top 10 domains of applications assessed for LLM trustworthiness over a six-year period. The data reveals significant growth in certain fields, particularly in the healthcare and education sectors.