为临床证据综合利用生成性人工智能需要确保可信度
Gongbo Zhang1, Qiao Jin2, Denis Jered McInerney3
1Columbia University, Department of Biomedical Informatics, New York, 10032, USA.
Journal of biomedical informatics
|April 12, 2024
概括
生成型人工智能可以帮助总结基于证据的医学医学证据. 确保人工智能模型是值得信赖的,公平的,包容的,对于可靠的医疗保健应用来说至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 基于证据的医学 基于证据的医学
背景情况:
- 改善医疗保健质量依赖于基于证据的医学.
- 医学文献的指数增长在证据合成方面带来了挑战.
- 生成型人工智能为管理庞大的医疗数据提供了潜在的解决方案.
研究的目的:
- 探索生成AI在医学证据总结方面的可靠性.
- 讨论开发医疗保健负责任和包容性AI模型的挑战.
主要方法:
- 讨论生成性AI能力的视角文章.
- 专注于医疗证据的自动总结.
- 对AI可信度,公平性和包容性的分析.
主要成果:
- 生成型人工智能,特别是大型语言模型,在自动化医学证据总结方面表现有前途.
- 在创建负责任,公平和包容的AI系统方面存在重大挑战.
- 可信度是人工智能在基于证据的医学中采用的关键考虑因素.
结论:
- 生成型人工智能可以帮助合成医学证据,支持基于证据的医学.
- 需要进一步的研究和开发,以确保人工智能的可信度和在医疗保健中的道德部署.
- 解决公平性和包容性对于可靠的AI应用在医学中至关重要.
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