Leveraging human-AI collaboration to visualize age-related diabetes features using dataset

This paper explores the synergy between humans and generative AI in the context of diabetes and biochemical analysis for endocrinologists. It underscores the necessity for human intervention to supplement the information that the AI has not yet learned, using search engines as a tool. The paper is crafted to be user-friendly, catering to both novices and those without a programming background. It covers human-centric for code verification, while the generative AI is tasked with creating Python code for data visualization automatically. The paper introduces a succinct set of guidelines for interacting with these AI tools, with the aim of minimizing unnecessary interactions. It guides readers on how to harness the power of the latest generative AI to assist and expedite research, using various search operators or options. While acknowledging the limitations of these generative AI tools, the paper emphasizes their potential in streamlining scientific research by reducing time and cost. It provides tangible examples, such as the visualization of graphs for the HbA1c dataset. In conclusion, despite their limitations, the paper champions the use of generative AI tools to propel advancements in science and technology. It highlights their significant potential in reducing time and cost, thereby catalyzing the pace of research.