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机器学习与癌症相遇

Elena V Varlamova1, Maria A Butakova1, Vlada V Semyonova2

  • 1The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova Str., 344090 Rostov-on-Don, Russia.

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|March 28, 2024
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概括

机器学习 (ML) 是人工智能 (AI) 的一部分,在瘤学中越来越重要,用于更快的诊断和治疗计划. 机器学习增强了医学图像分析,预后预测和药物合成,改善了患者的护理,尽管存在道德挑战.

关键词:
聚乙烯/聚乙烯/聚乙烯人工智能的人工智能是人工智能.机器学习是机器学习.瘤学 在瘤学方面.无线电学 (radiomics) 是一种无线电学.

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科学领域:

  • 在瘤学瘤学.
  • 人工智能的人工智能
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 人工智能 (AI),特别是机器学习 (ML) 在瘤学的应用正在迅速扩大.
  • ML的整合有望加速癌症护理中的诊断和治疗规划过程.
  • 医疗保健中大数据的数量不断增加,需要像ML这样的先进分析工具.

研究的目的:

  • 审查最近机器学习在瘤学中的应用.
  • 突出ML在医学图像分析,治疗计划,预后和药物合成中的作用.
  • 讨论人工智能在癌症研究和医学中的未来前景和伦理考虑.

主要方法:

  • 关于机器学习在瘤学中的应用最新文献的综述.
  • 分析ML对诊断准确性和治疗疗效的影响.
  • 探索ML在药物发现和个性化医学的潜力.

主要成果:

  • 与传统方法相比,机器学习模型已经证明了更好的预后预测.
  • ML促进了医疗图像的更快,更可靠的分析,这对于侵袭性癌症至关重要.
  • 通过大数据分析,ML提高了处方治疗和患者护理的质量.
  • ML显示了在护理地点直接合成医疗物质的潜力.

结论:

  • 机器学习将成为瘤学家和医疗专家必不可少的技术.
  • 人工智能驱动的工具为推进癌症研究和其他医学领域提供了巨大的潜力.
  • 解决尚未解决的伦理和法律问题对于在医学中广泛采用AI至关重要.