人工智能转化为瘤学临床实践
Issam El Naqa1, Aleksandra Karolak2, Yi Luo2
1Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA. issam.elnaqa@moffitt.org.
Oncogene
|September 8, 2023
概括
人工智能 (AI) 和机器学习 (ML) 通过改进诊断和药物发现来彻底改变瘤学. 然而,验证和实施方面的挑战阻碍了这些强大的AI工具的广泛临床采用.
科学领域:
- 生物医学和健康科学 生物医学和健康科学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 准备好改变生物医学,为瘤学研究和临床实践提供解决方案.
- 潜在的应用包括工作流程自动化,个性化医疗和减少医疗保健差距.
研究的目的:
- 审查AI/ML临床翻译在瘤学的挑战和机遇.
- 突出具体的用例和成功实施的要求.
主要方法:
- 在瘤学中新兴AI/ML应用的文献综述.
- 在放射学,放射瘤学,免疫疗法和药物发现中分析临床用例.
- 检查临床翻译的挑战和实施的要求.
主要成果:
- 人工智能/ML的应用涵盖了癌症查,诊断,反应预测,预后和药物发现.
- 很少有AI/ML模型被验证或规范用于临床使用.
- 关键的挑战包括验证,监管批准和临床整合.
结论:
- 成功实施AI/ML需要多学科合作,领域专家参与,模型透明度和强有力的质量保证.
- 解决培训偏见和数据漂移对于在瘤学中安全和有益的AI/ML应用至关重要.
- 需要进一步的研究和开发,以克服临床翻译的障碍.
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