急性 (破坏性) 胰腺炎:使用人工智能预测结果,全面审查
K Osmonaliev1, S U Rehman1, E Kudayarov2
1Ala-Too International University, Faculty of Medicine, Bishkek, Kyrgyzstan.
Brazilian journal of biology = Revista brasleira de biologia
|October 22, 2025
概括
人工智能 (AI) 可以改善急性胰腺炎 (AP) 结果预测. 挑战包括数据质量,模型可解释性和偏见,但未来的人工智能应用对临床实践充满希望.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床预测模型临床预测模型
背景情况:
- 急性胰腺炎 (AP) 的严重程度范围广泛,需要准确的结果预测,以有效管理患者.
- 预测AP严重程度对于及时干预,资源分配以及降低患者发病率和死亡率至关重要.
- 传统的预测方法在捕捉AP进展的复杂性方面存在局限性.
研究的目的:
- 审查有关人工智能 (AI) 和机器学习 (ML) 预测急性胰腺炎 (AP) 模型的文献.
- 分析人工智能驱动AP预测工具的方法,评估标准和临床实用性.
- 确定在AP管理中对AI的当前挑战和未来方向.
主要方法:
- 对使用AI/ML进行AP结果预测的研究进行系统性文献综述.
- 分析报告的方法,性能指标和AI模型的临床适用性.
- 检查数据质量,模型解释性和算法偏差问题.
主要成果:
- 与传统方法相比,AI/ML模型显示了提高AP结果预测准确性的潜力.
- 发现的重大挑战包括数据质量不足,模型无法解释,以及算法固有的偏见.
- 讨论了诸如可穿戴设备的实时监控和可解释AI (XAI) 等新兴策略.
结论:
- 人工智能对改变急性胰腺炎预测和管理的临床方法具有重大前景.
- 解决数据限制,确保模型透明度和减轻偏见对于成功实施AI至关重要.
- 未来的研究应该专注于开发强大的,可解释的AI模型,并整合实时数据以提供个性化的AP护理.
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