Municipal Art Society of New York

This primer provides an overview of artificial intelligence (AI) for the oncologist and technologist, delineating its relevance, methodologies, and applications. AI, encompassing machine learning (ML) and deep learning (DL), utilizes algorithms to analyze data and make predictions, facilitating early cancer detection and personalized treatment strategies. ML, characterized by supervised and unsupervised learning, allows for pattern recognition and prediction, whereas DL, particularly through convolutional neural networks and transformers, excels in complex data analysis. AI applications span various oncology data types, including electronic health records, medical imaging (radiology and digital pathology), biomarkers (genomics, proteomics, and metabolomics), and digital health sensors. Leveraging AI in oncology enhances diagnostic accuracy, improves treatment selection, and enables early detection through innovative approaches such as wearable devices and mobile health applications. This primer underscores AI's transformative potential in advancing cancer care, exemplified by Food and Drug Administration-approved AI systems and prominent DL architectures. This overview serves as a guide for researchers, clinicians, and policymakers navigating the evolving landscape of AI-driven oncology, emphasizing its pivotal role in augmenting patient outcomes and shaping the future of cancer care.