Reimagining cancer treatments in the era of generative AI

Abstract

Significant advances in the treatment of cancer have been achieved as reflected by the ever-expanding space of cancer therapeutics being available to cancer patients. Often, however, it is not clear which patient would respond to which drug and what combination of drugs will improve patient outcomes. Furthermore, while many of these drugs are initially effective, therapeutic resistance is often inevitable due to the evolving nature of cancer. Generative artificial intelligence (GenAI) powered by the increasingly large amount of accumulating clinical, molecular, and radiomics data about cancer patients and their treatments may serve as the kernel of rapid learning decision-support systems that could enable personalized cancer treatments to counter therapeutic resistance and overcome the shortcomings of the current standard of care. This perspective is explored in the context of current advances of AI applications in oncology and the potential of GenAI learning and inferencing capabilities to support patient-tailored dynamic cancer treatments. A discussion of this vision is elaborated with respect to issues pertinent to GenAI use in real-world clinical settings, including clinical validation, data curation, and sharing, large language model hallucinations as well as ethical concerns and considerations such as privacy, bias, transparency, and accountability.

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