Earticle

초록

영어

The rapid advancement of artificial intelligence (AI) technology is driving innovative changes across the entire healthcare sector. To systematically identify the latest research trends in the field of Medical AI, this study collected abstracts from a total of 1,043 academic papers published between 2006 and 2026 and applied a BERTopic-based topic modeling method to identify and classify major research topics. The analysis revealed that Medical AI research is categorized into 35 specific topics, including medical education and the use of ChatGPT/LLMs, medical imaging and deep learning diagnostics, privacy protection and federated learning, AI explainability (XAI) and ethics, medical device regulation and legal liability, clinical data and disease prediction, IoMT and security, and COVID-19 and public health applications. The number of papers has shown an explosive increase since 2023. This study provides practical implications for setting future directions in Medical AI research and formulating policies.

목차

Abstract
1. INTRODUCTION
2. THEORETICAL BACKGROUND
2.1. Artificial Intelligence
2.2. Medical AI
2.3. BERTopic
3. RESEARCH PROCEDURES
3.1. Data Collection
3.2. Data Preprocessing
3.3. Analysis
3.4. Visualization
4. RESEARCH RESULTS
4.1. Overview of Key Topics Identified
4.2. Analysis of Major Topic Clusters and Content
4.3. Results of Topic Structure Analysis
5. CONCLUSION
REFERENCES

키워드

저자

• Chang Liu [ Department of Logistics Management and Engineering, Taisan University, Shandong, China ]
• Eun-Mi Park [ Strategic Planning Division, KASOM, Seoul, South Korea ]
• Seong-Taek Park [ Strategic Planning Department, CISTEP, Chungnam, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Intelligent Technologies and Innovative Practices

• 간기

  계간

• eISSN

  3092-412X

• 수록기간

  2026~2026

• 십진분류

  KDC 323 DDC 338