临床预测工具的可持续部署-对模型维护的360度方法
Sharon E Davis1, Peter J Embí1,2, Michael E Matheny1,2,3,4
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 29, 2024
概括
临床人工智能 (AI) 模型需要持续监测和维护,以确保它们的准确性和实用性随着时间的推移. 一个全面的框架通过预防性,先发性,响应性和反应性战略来解决这些挑战,以实现可持续的医疗保健人工智能.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 卫生系统管理管理卫生系统管理
背景情况:
- 人工智能 (AI) 越来越多地融入临床护理,这给可持续性模型带来了挑战.
- 不断变化的临床环境会导致数据集的变化,随着时间的推移,人工智能模型的预测准确性和实用性会降低.
研究的目的:
- 为临床AI模型的生命周期管理提出框架.
- 确保AI在医疗保健环境中的长期影响和可持续性.
主要方法:
- 描述了人工智能模型监控和维护的框架.
- 涵盖了预防性,先发性,响应性和反应性方法的360度连续性.
- 在卫生系统算法和警计划中整合战略.
主要成果:
- 四种拟议方法的详细补充优势和局限性.
- 突出了人工智能模型长寿协调战略的必要性.
结论:
- 对人工智能模型的监控和维护采取全面的,多角度的方法至关重要.
- 确保临床AI的长期承诺需要积极的生命周期管理.
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