Enterprise AI Analysis

Studies addressing the use of LLMs in contexts requiring trustworthiness, transparency, safety, and meaningful human oversight, including accessibility, education, and language technologies.

• Andruccioli et al. explore the role of LLMs in sustainable and inclusive web accessibility, showing how these models can support the identification of accessibility issues in dynamically generated web content that may be overlooked by conventional validation tools, while also discussing the risks associated with redundant or hallucinated warnings [12].
• Alostad investigates the use of LLMs as annotators for stance detection in the Kuwaiti dialect, demonstrating that carefully prompted open models can produce promising results even in a low-resource linguistic setting [13].
• Mitroulias and Sioutas present a systematic review and bibliometric analysis of automated multiple-choice question generation, offering a structured perspective on the development of this research area and its intersection with recent LLM-based methods [14].

Research on the promise and challenges of LLM adoption in critical healthcare environments, emphasizing privacy, reliability, accountability, and security.

• Hamid and Brohi provide a review of LLMs in healthcare, discussing major application categories together with threats, vulnerabilities, and security frameworks needed to support safer deployment in real-world medical contexts [15].
• Karami et al. analyze ChatGPT (GPT-3) prompts shared through social media discourse to identify health-related uses of AI chatbots, thus offering an interesting empirical perspective on how users perceive and employ these systems in everyday practice [16].

Investigations into generative AI's role in high-impact settings like cybersecurity and financial risk management, balancing effectiveness with interpretability and robustness.

• Daniel et al. compare machine learning models and LLMs for labeling network intrusion detection system rules with MITRE ATT&CK techniques, showing that LLMs provide interesting opportunities in terms of automation and explainability, while conventional machine learning approaches still maintain advantages in predictive accuracy [17].
• Roumeliotis et al. investigate LLMs and other NLP models for cryptocurrency sentiment analysis, comparing advanced language models for classifying the sentiment of crypto-related news and discussing their relevance for investment intelligence and risk management [18].