Empowering Public Health: <scp>AI</scp>‐Powered Security Solutions for <scp>AI</scp>‐Driven Challenges

Abstract

ABSTRACT The escalating integration of artificial intelligence (AI) in public healthcare has raised a critical concern: the vast amounts of data being generated and utilised by AI language models are not adequately connected to privacy and security considerations. This study addresses the problem by exploring how AI language models can be used to enhance digital security in public healthcare while addressing challenges related to privacy and ethics. The research adopts a three‐phase methodology: a bibliometric analysis of literature from the Scopus database to identify research trends, the generation of AI‐driven scenarios refined by healthcare professionals and analysing AI responses using grounded theory. Two scenarios, focused on AI‐driven clinical decision support systems and AI‐powered telemedicine platforms, were validated by healthcare experts and tested using ChatGPT‐4 and Gemini, two prominent AI models. While ChatGPT‐4 produced contextually specific and diverse responses, Gemini's outputs were inconsistent and repetitive, highlighting discrepancies in their performance. These discrepancies are linked to the data used to train these models, implying that incorporating more specialised healthcare data could enhance performance; however, such data usage must align with ethical guidelines. The analysis found that human, organizational, and technological dimensions are critical for addressing security issues and promoting trust in healthcare systems utilising AI. While AI‐generated scenarios are a valuable starting point, they must be mediated by medical professionals to ensure practical applicability. The findings provide a theoretical framework for handling AI‐generated issues related to privacy and security concerns, which can be used for future empirical research to enhance digital security in public healthcare.

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