人工智能辅助的心血管风险评估由资源有限的印度尼西亚环境中的全科医生使用概念原型:随机对照研究.
Anindya Pradipta Susanto1,2,3, David Lyell1, Bambang Widyantoro4
1Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, Sydney, 2113, Australia, 61 423309268.
Journal of medical Internet research
|November 25, 2025
概括
人工智能 (AI) 临床决策支持 (CDS) 在资源有限的环境中显著改善了动脉样硬化心血管疾病 (ASCVD) 风险评估和他类药物处方. 此外,AI-CDS还可以缩短医生的决策时间,提高初级保健的效率.
科学领域:
- 数字健康和人工智能 (AI) 的发展
- 预防心血管疾病预防心血管疾病
- 医疗信息学 医疗信息学
背景情况:
- 整合数字健康和人工智能的预防策略显示出减轻动脉样硬化心血管疾病 (ASCVD) 的前景.
- 人工智能支持的临床决策支持 (CDS) 提供了超越传统风险因素的患者特定见解.
- 在资源有限的环境中,AI-CDS对ASCVD风险管理的有效性尚未得到充分探索.
研究的目的:
- 评估基于AI的CDS对10年ASCVD风险评估和管理在初级预防中的影响.
- 将基于人工智能的CDS与自动化的CDS进行比较,并且在初级保健环境中没有决策支持.
主要方法:
- 这是一项涉及印度尼西亚102名医生的三方随机对照研究.
- 医生评估了10年的ASCVD风险和管理决策,在三个条件下对9个临床细节进行了评估:基于AI的CDS,自动化的CDS或没有支持.
- 结果包括正确的风险评估,患者管理 (阿司匹林,他类药物,抗高血压药物处方,转诊),决策时间和AI感知的实用性.
主要成果:
- 与没有支持相比,基于AI的CDS提高了27%的ASCVD风险评估和29%的他类药物处方.
- 在风险评估和他类药物处方方面,人工智能辅助决策显著更准确,正确的风险分类需要治疗3.7的数量.
- 人工智能辅助的病例需要更短的决策时间 (63.6s vs 72.8s),而阿司匹林/抗高血压药处方和转诊决定的改善在统计学上并不显著.
结论:
- 基于人工智能的CDS显示了改善ASCVD风险评估和在资源有限的初级保健机构处方他的巨大潜力.
- 通过AI-CDS减少决策时间,突出了它对有效利用医疗保健资源的价值.
- 需要进一步的研究来证实这些发现在低资源环境中的实际应用.
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