在AI应用中使用医疗推理的基础上进行考虑
Adamantios Koumpis1, Adam S L Graefe1,2
1Institute for Biomedical Informatics, University Hospital Cologne and Medical Faculty, University of Cologne, Cologne, Germany.
Frontiers in medicine
|October 3, 2024
概括
医学中的人工智能 (AI) 和机器学习 (ML) 面临着数据挑战. 使用小数据并保持临床推理的平衡方法对于有效的医疗决策至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 临床决策 - 临床决策
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在医学推理中的整合提供了显著的机遇.
- 训练人工智能系统需要大量的数据集,与临床医生可获得的有限数据形成鲜明对比.
- 越来越多地依赖技术,这引发了关于保持必要的临床推理技能的问题.
研究的目的:
- 探索AI/ML在医学推理和决策中的挑战和机遇.
- 倡导一种平衡的方法,将大规模数据与小数据策略相结合.
- 强调保持临床推理与技术进步并行的重要性.
主要方法:
- 对医疗保健中的AI/ML当前文献的审查和综合.
- 对人工智能培训与临床实践的数据需求分析.
- 讨论多学科研究方法.
主要成果:
- 在医学中整合AI/ML需要仔细考虑数据的可用性和使用情况.
- 结合小数据原则的混合方法对于强大的医疗人工智能至关重要.
- 在临床推理中,跨学科的协作是导航AI复杂性的关键.
结论:
- 在医学中有效地实施人工智能需要一个平衡的数据策略和保持临床判断.
- 精心抽象和概念建模对于成功的AI应用在医学推理中至关重要.
- 未来的研究应该专注于整合AI/ML,同时维护医学艺术.
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