General Artificial Intelligence for the Diagnosis and Treatment of Cancer: The Rise of Foundation Models

Abstract Artificial intelligence (AI) and its computer algorithms, including those of machine learning and deep learning, have been transforming the oncology field. Generative AI (Gen AI) and general AI technologies, which can be adapted to various tasks based on diverse inputs, are being increasingly utilized in healthcare applications. Foundation models, driven by Gen AI and general AI technologies, have enthralled the medical world, representing the next generation of large-scale AI tools in medicine that are trained on massive amounts of data and will make a major impact due to their versatility, high performance, personalization enhancement, and assistance of healthcare workers. That said, it is undeniable that there are multiple obstacles that these algorithms must overcome to be successfully utilized in the field. In this review, we first present a brief background and a history of the recent rise of generative AI and foundation models. Next, we explore their applications in the diagnosis and treatment of cancer, specifically in radiology, pathology, precision medicine, personalization of care, and surgical oncology. Then, we discuss some of the limitations that could hinder general and Gen AI’s clinical translation.