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Over the past 5 years, large language models (LLMs) have emerged and continued to improve in their generative abilities and are now capable of generating human-understandable text and performing complex data analyses. As these models continue to improve in their capabilities, they are increasingly used to support population oncology, including clinical information extraction, cancer care education, and clinical decision support. This narrative review provides a high-level description of the use of LLMs in cancer with an overview of the current literature, along with research gaps. Despite increasing interest in using LLMs for cancer care, prevention, and research, applied methods in cancer still lag advancements published in the computer science literature. Therefore, we recommend that cancer-focused LLM research and applications better incorporate technical advancements and techniques found in the computer science literature. Additionally, standardized evaluation metrics and approaches need to be better studied and adopted in oncology, along with data governance and computational infrastructure to support state-of-the-art model integration and the use of real-world data. Finally, we describe the need for researchers to incorporate principles and frameworks from implementation and dissemination science to promote LLM-based tool adaptation, effectiveness, fit, and sustainability.