人工智能与多式联络数据分析在瘤免疫疗法和向治疗中的价值
Dan Lv1, Sufei Wang1, Wenjing Xiao1
1Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, Key Laboratory of Respiratory Diseases of National Health Commission, State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China; Hubei Province Engineering Research Center for Tumor-Targeted Biochemotherapy, MOE Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China; Hubei Province Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China; Department of Translational Medicine Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China.
人工智能 (AI) 通过整合多式联络数据来改善癌症治疗,从而实现更好的预测. 挑战包括数据问题和模型透明度在人工智能驱动的精密医学.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 免疫疗法和向疗法代表了精确瘤学的重大进展.
- 人工智能 (AI) 对于预测治疗疗效和个性化癌症疗法越来越重要.
- 在瘤学中,早期的AI使用了有限的单模数据,限制了其分析能力.
研究的目的:
- 在癌症诊断和治疗中审查人工智能技术.
- 在瘤学中探索多式联络数据源和融合技术.
- 总结人工智能应用与瘤免疫疗法和向治疗中的多式数据,突出挑战.
主要方法:
- 对瘤学中常见的人工智能技术的审查.
- 分析多式联运数据源和融合策略.
- 使用综合数据检查AI在免疫治疗和向治疗中的应用.
主要成果:
- 人工智能与多式联网数据分析相结合,提高了诊断准确性和预后预测.
- 在瘤学中,个性化治疗规划通过人工智能和多式联络数据集成来优化.
- 人工智能有助于增强治疗疗效预测和新目标识别.
结论:
- 人工智能和多式联络数据融合在精确瘤学方面取得了重大进展,特别是在免疫治疗和向治疗方面.
- 主要挑战仍然存在,包括数据稀缺性,标准化,对齐以及深度学习模型的可解释性.
- 应对这些挑战对于AI在癌症治疗中的持续发展和临床实施至关重要.
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