在中使用人工智能:系统性审查
Almuntasar Al-Breiki1, Said Al-Sinani1, Ahmed Elsharaawy2
1College of Medicine and Health Sciences, Sultan Qaboos University, Muscat, Oman.
Journal of epilepsy research
|June 26, 2025
概括
人工智能和机器学习显示出改善的诊断和治疗的前景. 然而,广泛的临床采用需要更大的研究和更多的验证来克服当前的局限性.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 的诊断和管理带来了重大的临床挑战.
- 手术干预有好处,但往往涉及漫长的治疗途径.
- 探索先进的计算方法对于改善患者护理至关重要.
研究的目的:
- 系统地审查人工智能 (AI) 和机器学习 (ML) 在诊断和治疗中的应用.
- 评估ML在预测治疗反应和手术结果方面的潜力.
- 确定ML在管理中的当前局限性和未来方向.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,Web of Science等) 进行全面的文献搜索. 从2015年至2022年期间.
- 包括36项以英语为语言的原始研究,重点关注的ML.
- 研究的分类为诊断,治疗结果,手术候选人识别和手术结果预测.
主要成果:
- 机器学习模型利用了各种数据 (症状,脑部扫描) 和算法 (SVM,CNN).
- 一些模型表现出高预测准确度 (AUC高达0.99).
- 关键的局限性包括样本规模小,研究间独立验证不足.
结论:
- 机器学习在推进治疗方面具有重大潜力.
- 目前的研究受到数据限制和强有力的验证需求的限制.
- 未来的努力应集中在大规模的合作研究和临床整合的长期结果数据上.
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