Open Access
hongyi.xin@sjtu.edu.cnZHOU Jieli. E-mail: zhoujieli@sjtu.edu.cn
Corresponding author: XIN Hongyi. Tel: +86-21-34206045, E-mail: hongyi.xin@sjtu.edu.cn
Received December 12, 2024; Accepted December 24, 2024; Published December 30, 2024
Open Access
hongyi.xin@sjtu.edu.cnZHOU Jieli. E-mail: zhoujieli@sjtu.edu.cn
Corresponding author: XIN Hongyi. Tel: +86-21-34206045, E-mail: hongyi.xin@sjtu.edu.cn
Received December 12, 2024; Accepted December 24, 2024; Published December 30, 2024
This study presents an intelligent system for patient education based on large language model (LLMs) and multi agent collaboration, achieving end-to-end process management through five specialized agents (Ask Agent, Assessment Agen, Advice Agent, Arrangement Agent, and Assistance Agent). The system not only improves treatment efficiency, but also provides more precise personalized treatment plans for complex cases, demonstrating significant value in enhancing patient education quality.
Key Words: large language model; multi-agent system; artificial intelligence; patient education
ISSN: 3006-4236
Volume 1, Issue 4
December 2024
Pages: 1-64