Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges

被引:1
|
作者
Kang, Kai [1 ]
Yang, Yuqi [2 ]
Wu, Yijun [1 ]
Luo, Ren [1 ]
机构
[1] Sichuan Univ, West China Hosp, Canc Ctr, Div Thorac Tumor Multimodal Treatment, Chengdu, Sichuan, Peoples R China
[2] Sichuan Univ, West China Sch Med, Chengdu, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Large language model; Medical education; Bioinformatics; ChatGPT; Artificial intelligence;
D O I
10.1007/s10439-024-03554-5
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics.
引用
收藏
页码:2311 / 2315
页数:5
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