LLM-Based Agents Utilized in a Trustworthy Artificial Conscience Model for Controlling AI in Medical Applications

As Large Language Models (LLMs) become more widely used in decision-making contexts, they can enhance trustworthiness, reliability, and user satisfaction in AI applications. This paper proposes implementing an artificial conscience model as a control mechanism for AI-based systems in medical applications that utilize LLM-based AI agents. These models provide structured oversight to ensure that AI applications adhere to user-defined requirements, such as achieving a balance between accuracy and explainability. To demonstrate the practical application of this control mechanism, we present a case study using the Wisconsin Breast Cancer dataset. Our research highlights the effectiveness of LLM-based AI agents in tasks requiring monitoring and control. Furthermore, we emphasize the potential of the artificial conscience control model to improve transparency, adaptability, and user satisfaction in AI-driven decision-making contexts. By employing this mechanism, we aim to pave the way for more reliable, trustworthy, and user-centric AI systems.