儿童尾炎中人工智能增强的临床决策:人工智能生成的模型可以提高学员的诊断能力吗?
Anas Shikha1, Asem Kasem2, Win Sabai Phyu Han3
1Unit of Paediatric Surgery, RIPAS Hospital, Jalan Putera Al-Muhtadee Billah, Bandar Seri Begawan, BA1712, Brunei Darussalam. dr.a.n.shikha@gmail.com.
European journal of pediatrics
|December 27, 2023
概括
人工智能儿科尾炎决策树 (AiPAD) 模型显著提高了学员尾炎诊断准确度,达到专家水平. 这种人工智能辅助的方法增强了临床判断和患者护理.
科学领域:
- 儿科手术 儿科手术
- 医疗人工智能 医疗人工智能
- 诊断的准确性 诊断的准确性
背景情况:
- 儿童尾炎的诊断是具有挑战性的,因为呈现方式和主观评估各不相同.
- 对于初级医生来说,诊断尾炎的传统培训需要丰富的经验.
- 人工智能模型可以编码专家直觉,以加速初级临床医生的诊断技能发展.
研究的目的:
- 为了评估人工智能儿科尾炎决策树 (AiPAD) 模型的有效性.
- 提高医疗学员在儿科尾炎的诊断能力.
- 将训练员的表现与人工智能辅助和没有人工智能辅助的表现与专家监督员进行比较.
主要方法:
- 一项前性研究包括136名儿科患者 (0-12岁) 怀疑患有尾炎.
- 在成像之前,实习生收集了五个预定义的临床参数.
- 该AiPAD模型提供了独立的预测,对外科团队视而不见.
主要成果:
- 监督员准确率为91%,受训人员平均准确率为70%.
- 使用AiPAD的学员平均准确率达到了97%.
- 在专家临床感知和AiPAD预测之间发现了强烈的关联.
结论:
- 艾帕德模型显著提高了儿童尾炎的诊断准确性.
- 这种人工智能-人类混合方法接近专家级的性能.
- 在提高诊断技能,减少错误和改善患者治疗结果方面,AiPAD显得有前途.
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