Bibliometric Analysis of AI-Based Prototype Proposal for User Security Awareness in Healthcare

In the realm of public healthcare, integrating information technology (IT) must be judiciously balanced with heightened security awareness among users, given the escalating frequency of cyberattacks targeting this sector. Despite the availability of various product and service solutions aimed at enhancing user security awareness, these efforts have yet to yield optimal outcomes. There is a pressing need for innovative approaches to bolster healthcare user security awareness through IT, particularly leveraging the rapidly advancing field of artificial intelligence (AI). This study conducts a comprehensive review of prior research on the application of AI, specifically Large Language Models (LLM), within the domain of healthcare cybersecurity from 2014 to 2024. The objective is to ascertain the volume of publications, trace the evolution of publication trends, and assess the potential and positioning of research in this area. Employing a bibliometric analysis methodology, this study analyzes a dataset comprising 1000 related publications indexed by Google Scholar. The findings reveal that publications concerning applying LLM AI in healthcare cybersecurity constituted 12.82% in 2023, with a significant increase to 87.18% in 2024, representing a 6.8-fold rise. The mapping of publication developments is categorized into 24 clusters, with large language models, healthcare, retrieval-augmented generation, LLM, artificial intelligence, and cybersecurity emerging as the six most frequently discussed keywords in the research landscape. Consequently, this study underscores the substantial potential for current and future research on the application of AI in healthcare cybersecurity, advocating for the development of AI-based solutions to enhance healthcare user security awareness.