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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Mapping the Landscape of Medical AI Research in Korea Using Topic Modeling.

Heejang Yun1, Yoonhee Lee2

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Summary

Korean medical artificial intelligence (AI) research has evolved from algorithm focus to integrated, patient-centered applications. Future directions emphasize explainable AI (XAI), collaboration, and ethical governance for AI in healthcare.

Keywords:
artificial intelligencekeyword network analysismedical AIresearch trendstopic modeling

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Bibliometrics

Background:

  • Analysis of 10 years (2015-2024) of domestic medical AI research in Korea.
  • Identified three chronological research stages: Introduction, Expansion, and Post-ChatGPT.
  • Research focus shifted towards AI integration in clinical and healthcare service domains.

Purpose of the Study:

  • To analyze trends and evolution in Korean medical AI research.
  • To identify dominant research themes and structural shifts.
  • To understand the maturation of medical AI towards human-centered applications.

Main Methods:

  • Utilized topic modeling and keyword network analysis on 686 papers from the Korea Citation Index (KCI).
  • Data preprocessing included stopword removal, synonym unification, and lemmatization.
  • Extracted and analyzed 7489 unique terms to map research landscapes.

Main Results:

  • Three dominant research themes emerged: Diagnostic Imaging/Algorithm Validation, Healthcare Service/System Integration, and Patient-Centered Prediction/Disease Modeling.
  • Keyword network analysis indicated a shift from algorithm-centric to system-level and patient-focused research.
  • Research trends reflect increasing complexity and integration of AI in healthcare.

Conclusions:

  • Korean medical AI research is advancing towards interpretability, integration, and human-centered paradigms.
  • Highlights the growing importance of explainable AI (XAI) for clinical adoption.
  • Emphasizes the need for multidisciplinary collaboration and robust governance for ethical AI deployment.