Khorsandi Danial, Farahani Amin, Zarepour Atefeh, Khosravi Arezoo, Iravani Siavash, Zarrabi Ali
Terasaki Institute for Biomedical Innovation Woodland Hills California 91367 USA.
Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences Tehran Iran.
RSC Adv. 2025 Aug 4;15(34):27795-27815. doi: 10.1039/d5ra03747f. eCollection 2025 Aug 1.
The integration of artificial intelligence (AI) in targeted anticancer drug delivery represents a significant advancement in oncology, offering innovative solutions to enhance the precision and effectiveness of cancer treatments. This review explores the various AI methodologies that are transforming the landscape of targeted drug delivery systems. By leveraging machine learning algorithms, researchers can analyze extensive datasets, including genomic, proteomic, and clinical data, to identify patient-specific factors that influence therapeutic responses. Supervised learning techniques, such as support vector machines and random forests, enable the classification of cancer types and the prediction of treatment outcomes based on historical data. Deep learning approaches, particularly convolutional neural networks, facilitate improved tumor detection and characterization through advanced imaging analysis. Moreover, reinforcement learning optimizes treatment protocols by dynamically adjusting drug dosages and administration schedules based on real-time patient responses. The convergence of AI and targeted anticancer drug delivery holds the promise of advancing cancer therapy by providing tailored treatment strategies that enhance efficacy while minimizing side effects. By improving the understanding of tumor biology and patient variability, AI-driven methods can facilitate the transition from traditional treatment paradigms to more personalized and effective cancer care. This review discusses the challenges and limitations of implementing AI in targeted anticancer drug delivery, including data quality, interpretability of AI models, and the need for robust validation in clinical settings.
人工智能（AI）在靶向抗癌药物递送中的整合代表了肿瘤学领域的一项重大进展，为提高癌症治疗的精准性和有效性提供了创新解决方案。本综述探讨了正在改变靶向药物递送系统格局的各种人工智能方法。通过利用机器学习算法，研究人员可以分析包括基因组、蛋白质组和临床数据在内的大量数据集，以识别影响治疗反应的患者特异性因素。支持向量机和随机森林等监督学习技术能够根据历史数据对癌症类型进行分类并预测治疗结果。深度学习方法，特别是卷积神经网络，通过先进的成像分析有助于改善肿瘤检测和特征描述。此外，强化学习通过根据患者实时反应动态调整药物剂量和给药时间表来优化治疗方案。人工智能与靶向抗癌药物递送的融合有望通过提供量身定制的治疗策略来推进癌症治疗，这些策略在提高疗效的同时将副作用降至最低。通过增进对肿瘤生物学和患者个体差异的理解，人工智能驱动的方法可以促进从传统治疗模式向更个性化、更有效的癌症护理的转变。本综述讨论了在靶向抗癌药物递送中实施人工智能的挑战和局限性，包括数据质量、人工智能模型的可解释性以及在临床环境中进行有力验证的必要性。
