利用生成性人工智能和大型语言模型:医疗整合的全面路线图
1School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
Healthcare (Basel, Switzerland)
|October 28, 2023
概括
生成型人工智能 (AI) 和大型语言模型 (LLM) 可以改变医疗数据管理. 非AI专业人士需要指导,以负责任有效地整合这些强大的工具.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 健康 数据科学 数据科学
背景情况:
- 生成型人工智能和LLM显示了彻底改变医疗保健数据和信息管理的潜力.
- 在指导非人工智能专业人员将这些技术纳入临床实践的文献中存在很大的差距.
- 现有研究探讨了机遇和挑战,包括道德和法律方面的考虑.
研究的目的:
- 解决在医疗保健中整合生成AI和LLM的指导需求.
- 阐明生成性AI和LLM的机制,将其与传统AI区分开来.
- 告知利益相关者关于负责任的采用,以改善医疗保健结果.
主要方法:
- 范围范围的文献审查,以识别和综合现有研究.
- 对生成AI和LLM机制的分析,包括从人类反中强化学习 (RLHF).
- 审查利益相关者参与和共同设计流程.
主要成果:
- 生成型人工智能和LLM在医疗数据管理,信息检索和决策方面取得了进展.
- 像RLHF这样的技术,少数射击学习和链式思维推理是关键的区分因素.
- 涉及临床医生和消费者的包容性联合设计对于成功的整合至关重要.
结论:
- 在数据,模型调整,及时策略和实施方面持续创新至关重要.
- 积极的组织参与道德和法律指导方针对于负责任的应用至关重要.
- 在提高医疗保健质量,安全性和效率方面,LLM具有前景.
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