IntroductionArtificial intelligence (AI) has been influencing the way oncology has been practiced. Major issues constituting a bottleneck are the lack of data for training purposes, confidentiality preventing development, or the absence of transparency in clarifying how models operate to generate decisions.Novel ModelsWith explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute to crucial dataset’s learning information. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economic burden to the underling system.FocusThis review focuses on new AI models that could make medical diagnosis more precise, quicker and convenient, as well as help with the discovery of new drugs and better selection of certain cancer therapies, in particular for those drugs whose results have been inconsistent across different groups of patients. We then speculate on the transformative role AI could play in predicting ADC therapy efficacy. This would ultimately bestow the medical field of an impeccable selection tool. Such colossal methodology, coupled with seeming monitoring of treatment and recovery, may be granting remedies that have been so longed, and deemed necessary.
引言 人工智能（AI）正深刻影响着肿瘤学的临床实践方式。当前发展的主要瓶颈包括训练数据不足、数据保密性限制模型开发，以及模型决策过程缺乏透明度等问题。 新型模型 可解释人工智能的应用有望消除临床医生、研究人员及患者对AI的信任与使用障碍。通过联邦学习的实施，多个机构可共同参与关键数据集的信息学习。精准诊断与正确用药对于避免不必要的生命损失、减轻医疗系统的经济负担至关重要。 研究重点 本综述聚焦于新型AI模型，这些模型能够提升医疗诊断的精准性、速度与便捷性，并助力新药研发与癌症治疗方案优化，尤其针对那些在不同患者群体中疗效存在差异的药物。我们进一步探讨了AI在预测抗体偶联药物（ADC）疗效中可能发挥的革命性作用，这最终将为医疗领域提供精准无误的治疗选择工具。这种宏大的方法体系，结合治疗与康复过程的实时监测，有望带来人们期待已久且迫切需要的医疗解决方案。
AI-Based Cancer Models in Oncology: From Diagnosis to ADC Drug Prediction
肿瘤(癌症)患者之家