Review and bibliometric analysis of AI-driven advancements in healthcare

Abstract

Purpose: This research intends to use literature review and bibliometric analysis methods to visually review the development status and important historical milestones of Artificial Intelligence, as well as the basic research, key topics, and future potential research hot spots of AI in the healthcare field. Methodology: Conduct in-depth analysis of AI in healthcare through bibliometrics methods such as publication activity analysis, co-occurrence analysis, and co-authorship analysis. Findings: This study outlines the development time trajectory of AI technology and its application in healthcare. Research shows that "algorithm", "machine learning", "deep learning", "controlled study", "major clinical study" and "healthcare delivery" as well as "decision support systems" are key topics for research. Gender-related research and ethical issues are areas of future focus. Research implications: The practical significance is that it can clarify and optimize the key directions of AI to improve the quality of medical decision-making, improve diagnostic accuracy and guide market investment. The originality is reflected in the comprehensive analysis of the development trajectory of AI in the medical and health field. Through a unique perspective and systematic approach, it provides an important reference for research trends and future directions in the field.