人工智能和机器学习能力在急性舟状骨骨折检测中的应用综述。

IF 1.6
Robert Miller, Laurence Jackson, Dijana Vilic, Louis Boyce, Haris Shuaib
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引用次数: 0

摘要

本文讨论了目前关于人工智能和机器学习模型在急性明显和隐匿性舟状骨骨折诊断中的潜在应用的文献。目前的研究有明显的方法学缺陷,存在较高的偏倚风险,无法与临床医生的表现进行有意义的比较(目前的参考标准)。在未来的研究中,具体的领域应该得到解决,以帮助推进人工智能在x线片解释中的有意义和临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.

Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.

Artificial intelligence and machine learning capabilities in the detection of acute scaphoid fracture: a critical review.

This paper discusses the current literature surrounding the potential use of artificial intelligence and machine learning models in the diagnosis of acute obvious and occult scaphoid fractures. Current studies have notable methodological flaws and are at high risk of bias, precluding meaningful comparisons with clinician performance (the current reference standard). Specific areas should be addressed in future studies to help advance the meaningful and clinical use of artificial intelligence for radiograph interpretation.

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