大型语言模型对医疗保健专业人员和科学家的实际应用
Florian Reis1, Christian Lenz1, Manfred Gossen2,3
1Medical Affairs, Pfizer Pharma GmbH, Berlin, Germany.
JMIR medical informatics
|September 5, 2024
概括
大型语言模型 (LLM) 为医疗保健和研究专业人员提供了实际的好处. 本指南详细介绍了有效整合的六个用例和策略,提高生物医学环境中的生产力和效率.
科学领域:
- 生物医学研究的研究.
- 医疗保健信息学 医疗保健信息学
- 人工智能应用的人工智能应用.
背景情况:
- 医疗专业人员和科学家对大型语言模型 (LLM) 的兴趣日益增长.
- 由于有限的用户体验,整合人工智能工具的挑战,导致初始结果不足最佳.
- 在医疗保健和研究中有效和安全地利用LLM的实际策略的需要.
研究的目的:
- 为科学家和医疗保健专业人员提供有限的LLM经验,实用,易于实施的用例.
- 讨论在生物医学中实施人工智能工具的一般提示策略和预防措施.
- 突出LLMs在提高工作场所生产力和效率方面的潜力.
主要方法:
- 专注于一个观点文章格式.
- 识别和讨论六个关键的LLM用例.
- 包括一般的提示策略和安全考虑.
主要成果:
- 确定了六个实际的LLM用例:定制翻译,文本精细化和信息提取,生成概述和见解,想法编译,个性化的教育材料创作和智力训练.
- 讨论人工智能实施的一般提示策略和预防措施.
- 强调提高生产力和效率的潜力.
结论:
- 法律学士学位可以显著提高医生和研究人员的生产力和效率.
- 通过实际的用例和策略解决用户体验差距对于成功的AI集成至关重要.
- 为了在生物医学中安全有效地使用人工智能工具,需要仔细实施和遵守预防措施.
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