Earticle

초록

영어

We purposed to enhance the accessibility and accuracy of alopecia counseling by proposing a retrievalaugmented generation (RAG) chatbot built on a large language model (LLM). The system integrates curated domain knowledge and empathy-oriented prompts to generate reliable and patient-friendly responses. A domain-specific dataset of approximately 1.33 million words was organized hierarchically and indexed with relevant keywords to support retrieval precision. To evaluate performance, 100 representative queries across five counseling categories were answered by both a baseline LLM and the proposed RAG-LLM. An external domain expert rated each response on factual accuracy, hallucination, clarity, and coverage. The RAG-LLM achieved higher factual accuracy (95% vs. 70%), lower hallucination (3% vs. 25%), and improved clarity and coverage (4.5 vs. 3.3). Statistical analysis confirmed the significance of these differences. The results demonstrate that platform-native RAG implementation can improve factual reliability and communication quality without external retrieval infrastructure, providing a practical framework for developing trustworthy, empathetic, and accessible digital health counseling systems.

목차

Abstract
1. Introduction
2. Background Theory
3. Dataset Construction
3.1 Data Sources
3.2 Characteristics and Structuring
3.3 Preprocessing and Structuring
4. System Design and Evaluation
4.1 System Architecture
4.2 Dataset Integration
4.3 Prompt and Response Design
4.4 Evaluation Protocol and Results
4.5 Statistical Testing
5. Conclusion
References

키워드

저자

• Jongbeom Ku [ Doctoral Course, Department of Immersive Convergence Content, Kwangwoon University, Korea ]
• Gwangmi Cho [ Doctoral Course, Department of Immersive Convergence Content, Kwangwoon University, Korea ]
• Hobyung Chae [ Professor, Industry-Academic Cooperation Foundation, Kwangwoon University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380