ВЕЛИКІ МОВНІ МОДЕЛІ ШТУЧНОГО ІНТЕЛЕКТУ В МЕДИЦИНІ

Abstract

This study conducts a thorough analysis of the current state and prospects of artificial intelligence (AI) technologies, particularly focusing on large language models (LLMs) and their implementation in healthcare. In an age of rapid digital technology advancement, this research is crucial for understanding the potential influences of AI on medical practices and research. A diverse range of methods, including analysis and synthesis, comparison, generalization, induction and deduction, systematization, as well as the phenomenological method, were employed for a comprehensive analysis. These techniques enabled not only an in-depth examination of the technical aspects of AI application but also facilitated an evaluation of its prospective impact on the field of medicine. The paper highlights the essential role of integrating large language models into the medical field. These models are not only capable of substantially enhancing the efficiency of processing vast medical data but also play a fundamental role in refining diagnostic procedures and reforming clinical decision-making processes. Furthermore, the article examines potential challenges associated with the use of AI in healthcare, particularly focusing on concerns regarding transparency, privacy, bias, and accountability These issues demand meticulous attention and effective solutions to ensure the successful integration of AI into medical practices. The research includes a complex, interdisciplinary approach surrounding the field of medicine, informatics, ethics, and law, underscoring the synergy among these diverse knowledge domains for the effective understanding and utilization of AI in healthcare. Moreover, the article underscores the present status and the prospective evolution of large language models within the medical sphere, emphasizing their significance and the imperative for ongoing research in this area. In summary, the authors support a holistic strategy for integrating AI into the medical sector. This strategy involves crafting models that prioritize personal data protection, generating high-quality and representative datasets for training, establishing ethical guidelines, and formulating relevant standards and legal frameworks. Additionally, the paper stresses the necessity of addressing technical challenges and innovating new methodologies for assessing AI's efficacy. The significance of this research is underscored by its potential benefits and hurdles associated with AI's incorporation into healthcare, highlighting the critical need for the medical community to be prepared for these evolving dynamics.