The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.
人工智能（AI）的革命性发展极大地推动了医学研究领域在预测治疗效果方面的进步。利用AI寻找更优疗法预测方法的迫切需求，促进了新模型的演进，使其在乳腺癌检测等临床场景中更具适用性。然而，在某些情况下，美国食品药品监督管理局不得不撤回部分先前获批的AI预后模型，因为这些模型最终可能对特定有心力衰竭风险的患者产生不准确的预后判断。鉴于医学研究界对AI进展及其医疗应用潜力常抱有不切实际的期待，建立基于AI的癌症模型标准化实施流程至关重要。具体而言，模型需满足标准化通用参数要求，确保逻辑模块的透明度，并避免算法偏见。本文综述了当前基于AI的预后方法研究进展，探讨了其在预测癌症患者抗体药物偶联物疗效方面的未来应用前景，并总结了近期使用此类偶联物进行癌症治疗的晚期临床试验结果。
肿瘤(癌症)患者之家