AI-based Cancer Models for Prediction of Antibody-Drug Conjugate (ADC) Response in Oncology

Artificial 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. With explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute crucial dataset’s learning information, without breaching confidentiality, to build more accurate and precise models, that would be otherwise impossible to generate. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economical burden to the underling system. Antibody-Drug Conjugates (ADC) offer efficient targeted delivery of cytotoxic payloads specific to cancer cells. The new AI models discussed in this review could make ADC selection methods thrive towards an impeccable selection method for patients, coupled with seeming monitoring of treatment and recovery.