深度学习系统在医疗保健中的未来现实世界的实施:以实施科学为指导的系统性审查
Rachel Marjorie Wei Wen Tseng1, Li Cheng Ong2, Jocelyn Hui Lin Goh3,4,5
1Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
在医疗保健中实施现实世界的深度学习 (DL) 对患者护理和工作流程有很大的前景. 然而,为了成功地采用DL系统,需要对成本,可持续性和利益相关者的接受进行更多的研究.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 医疗信息学医学信息学
- 医学中的人工智能
背景情况:
- 医疗保健中的深度学习 (DL) 应用正在超越初步研究.
- DL对患者护理和临床工作流程的实际影响需要进一步调查.
- 了解DL工具在临床环境中的性能对于有效部署至关重要.
研究的目的:
- 系统地审查未来DL研究的实施策略和结果.
- 发现当前DL实施研究中的差距.
- 为未来的DL系统实施提出建议.
主要方法:
- 使用分层方法和实施科学框架的系统审查.
- 包括20个潜在的DL实施研究在放射学,耳鼻喉学,皮肤学和眼科.
- 临床结果的评估,采用,适当性,成本,可持续性和利益相关者的可接受性.
主要成果:
- 所有审查的研究都证明了DL系统在临床工作流程中的有效性和可行性.
- 采用和适当性是最常被评估的实施结果.
- 关于实施成本,可持续性和利益相关者接受度的研究有限.
结论:
- 未来的现实研究表明DL系统在临床实践中是有效和可行的.
- 在评估DL实施的经济和长期方面存在重大差距.
- 进一步的混合有效性实施研究对于在医疗保健中无采用DL至关重要.
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