GenAI의 이해와 헬스케어 분야에서 활용

Abstract

The emergence of Generative Artificial Intelligence (GenAI), exemplified by ChatGPT, has marked a new phase in AI technology. GenAI utilizes advanced neural network models like Generative Pre-trained Transformers (GPT) to produce human-like text, images, and other creative outputs. This study explores the underlying principles of GenAI, focusing on large language models (LLMs) and transformer architectures that enable context-aware and probabilistic generation. Applications of GenAI span various domains, including healthcare, education, and research. Key examples include AI-driven disease prediction, personalized treatment planning, and acceleration of medical research with tools like AlphaFold and PathAI. In education, GenAI aids personalized learning and automates administrative tasks, enhancing teaching efficiency. Prompt engineering is emphasized as a crucial skill for optimizing GenAI results, requiring clarity, specificity, and context. A comparative analysis of major GenAI tools highlights their performance in benchmarks such as multitask reasoning and cost efficiency. Despite its potential, GenAI presents challenges such as data biases, ethical considerations, and risks of misuse. The study underscores the importance of AI literacy and interdisciplinary approaches to maximize its benefits while addressing limitations. Recommendations include integrating GenAI into education and fostering essential skills for leveraging this transformative technology.