探索ChatGPT在选择2型糖尿病抗糖尿病药物的价值
Seol A Jang1, Su Jin Kwon1, Chul Sik Kim1
1Department of Endocrinology, Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea.
Diabetes, obesity & metabolism
|July 23, 2025
概括
像ChatGPT这样的大型语言模型 (LLM) 在帮助选择2型糖尿病 (T2DM) 的抗糖尿病药物方面表现有前途. 虽然在简单的疗法中有效,但在复杂的治疗中,准确性会降低,因此需要进一步开发临床用途.
科学领域:
- 人工智能在医学中的应用
- 内分泌学和新陈代谢学
- 药物治疗 药物治疗
背景情况:
- 2型糖尿病 (T2DM) 管理需要仔细选择抗糖尿病药物.
- 大型语言模型 (LLM) 提供了临床决策支持的潜力.
- 在药物选择中评估LLM绩效对于临床整合至关重要.
研究的目的:
- 评估在T2DM中使用LLM,特别是GPT,用于选择抗糖尿病药物的可行性和有效性.
- 使用个人临床数据,将LLM建议与临床医生的处方进行比较.
主要方法:
- 对永仁医院内分泌部T2DM患者的回顾性研究.
- 利用ChatGPT 4.0与零射击和少数射击学习进行治疗建议.
- 在LLM和临床医生处方 (单疗法,双疗法,三疗法) 之间分类一致.
主要成果:
- 单独治疗的总体一致性最高,并且随着治疗方案的复杂性而下降.
- 在未经治疗的患者中,单一治疗的同意率为69.2% (第一),69.2% (第二) 和84.6% (第三).
- 少数注射促使比零注射更好的协议,特别是单疗法和双疗法,但三重疗法显示的准确性有限.
结论:
- 聊天GPT显示出作为一种决策支持工具的潜力,用于选择抗糖尿病药物,特别是对于未经治疗的患者.
- 短期学习提高了对更简单的治疗方案的建议准确性.
- 对于复杂的治疗方案,如三重疗法,LLM的准确性是有限的,因此需要进一步细化临床应用.
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