整合人工智能的挑战和机会在放射性瘤学:一个叙事审查
Chiyoung Jeong1, YoungMoon Goh1, Jungwon Kwak1
1Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Ewha medical journal
|July 24, 2025
概括
人工智能 (AI) 通过优化治疗计划,图像分析和适应性治疗来增强辐射瘤学. 解决数据,监管和实施方面的挑战是推动人工智能在放射治疗中的患者护理的关键.
科学领域:
- 医学物理 医学物理
- 在瘤学瘤学.
- 计算机科学 计算机科学
背景情况:
- 人工智能 (AI) 越来越多地融入医疗领域.
- 放射性瘤学为人工智能驱动的进步提供了重大机会.
研究的目的:
- 审查人工智能在辐射瘤学中的整合.
- 突出与人工智能采用相关的挑战和机会.
- 探索AI对治疗精度,效率和结果的影响.
主要方法:
- 在辐射瘤学中对AI应用的文献综述.
- 分析AI在治疗计划,图像分析,适应性治疗和预测分析中的作用.
- 讨论包括数据质量,互操作性,监管和临床实施在内的挑战.
主要成果:
- 人工智能通过改进的治疗计划,通过先进的图像分析 (CT,MRI,PET) 的增强瘤检测/细分以及实时适应性调整来优化放射治疗.
- 由AI驱动的预测分析可以预测治疗结果和并发症,从而实现个性化医疗.
- 关键的挑战包括数据质量,标准化,伦理/监管障碍和临床接受.
结论:
- 在放射性瘤学中整合人工智能为改善患者护理和运营效率提供了巨大的潜力.
- 克服实施挑战需要研究人员,临床医生和行业利益相关者之间的合作.
- 未来的研究和临床实践应该专注于利用人工智能用于先进的放射治疗解决方案.
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