Artificial intelligence (AI) is increasingly being applied to clinical cancer research, driving precision oncology objectives by gathering clinical data at scales that were not previously possible. Although small, domain-specific models have been used toward this end for several years, general-purpose large language models (LLMs) now enable scalable data extraction and analysis without the need for large, labeled training data sets. These models support several applications, including building clinico-omic databases, matching patients to clinical trials, and developing multimodal foundation models that integrate text, imaging, and molecular data. LLMs can also streamline research workflows, from automating documentation to accelerating clinical decision making. However, data privacy, hallucination risks, computational costs, regulatory requirements, and validation standards remain significant considerations. Careful implementation of AI tools will therefore be an important task for cancer researchers in coming years.