对于免疫瘤学来说,AI/ML会有多大的变革?
Daniel Bottomly1, Shannon McWeeney2
1Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA.
Journal for immunotherapy of cancer
|March 26, 2024
概括
人工智能和机器学习 (AI/ML) 为推进免疫瘤学研究和临床应用提供了强大的工具. 本综述探讨了AI/ML如何解决数据分析,工作流效率和队列研究中的关键挑战,以获得更好的癌症免疫治疗结果.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物医学研究生物医学研究
- 临床操作 临床操作
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 免疫瘤学利用患者的免疫系统对抗癌症.
- 技术进步在生物医学研究和临床操作中产生了大量的数据.
- 人工智能和机器学习 (AI/ML) 显示出改善免疫瘤学的洞察力和结果的巨大潜力.
研究的目的:
- 审查评估AI/ML在免疫瘤学的临床影响的关键考虑因素.
- 突出免疫瘤学的关键临床和生物医学挑战.
- 探索AI/ML进步如何解决这些挑战.
主要方法:
- 审查当前的AI/ML应用及其与免疫瘤学的相关性.
- 确定和讨论免疫瘤学中的四个具体挑战:临床工作流程效率,图像数据策划,文本知识综合和小队列规模.
- 探索未来的AI/ML进步,包括强化学习和联合学习,以及道德数据实践.
主要成果:
- 人工智能/ML有可能提高临床工作流程的效率.
- 人工智能/ML可以帮助编制高质量的图像数据用于研究.
- 人工智能/机器学习有助于从文本数据中提取和合成知识.
- 在免疫治疗评估中,AI/ML方法可以帮助解决与小队列大小相关的挑战.
结论:
- AI/ML对促进免疫瘤学研究和临床实践具有重大前景.
- 通过AI/ML解决数据质量,工作流和队列分析方面的挑战至关重要.
- 未来的AI/ML创新,再加上伦理数据实践,很可能会推动免疫瘤学的重大进展.
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