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Integrating large and diverse data streams into timely, patient\u2010specific decisions provides a compelling rationale for incorporating artificial intelligence (AI) into cancer care. Generative artificial intelligence (GenAI) encompasses several classes of models capable of generating new content. This clinically focused review concentrates on GenAI applications based primarily on large language models (LLMs) and vision\u2010language models (VLMs), as well as emerging agentic systems that use these models to perform coordinated, multistep tasks. LLMs demonstrate substantial medical knowledge, strong performance on oncology\u2010related assessments, and emerging capabilities in clinical reasoning. Guideline\u2010 and retrieval\u2010augmented systems can improve concordance with expert recommendations and interpretation of complex treatment algorithms. LLMs and VLMs have been applied to documentation, patient communication, information extraction and summarization, clinical decision support, and multimodal data synthesis. However, performance varies substantially across tasks and clinical domains, with important limitations including incomplete or outdated information, training\u2010data bias, automation bias, and confabulation. Agentic AI represents an emerging extension of these technologies in which LLMs or VLMs are incorporated into systems capable of multistep planning, external information retrieval, specialized tool use, and iterative evaluation and refinement. Early oncology applications include evidence synthesis, multimodal clinical decision support, radiology, pathology, surgery, and individualized treatment planning. With rigorous validation, transparent implementation, continual updating, and appropriate expert oversight, GenAI may augment oncologists\u2019 judgment, improve patient understanding and experience, streamline cancer care delivery, and advance personalized oncology.<\/jats:p>","DOI":"10.1002\/cncr.70652","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T17:44:06Z","timestamp":1792777446000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative artificial intelligence in medical oncology: A clinically focused narrative review of large language, vision\u2010language, and agentic artificial intelligence systems"],"prefix":"10.1002","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3474-4609","authenticated-orcid":false,"given":"Harry B.","family":"Burke","sequence":"first","affiliation":[{"name":"George Washington University School of Medicine  Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2025.2888"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(23)01576\u20103"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.23.02439"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/ssu.2980100111"},{"key":"e_1_2_7_6_1","first-page":"1063","volume-title":"Advances in Neural Information Processing Systems 7","author":"Burke HB","year":"1995"},{"key":"e_1_2_7_7_1","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov T","year":"2013","journal-title":"arXiv"},{"key":"e_1_2_7_8_1","author":"Vaswani A","year":"2017","journal-title":"arXiv. arXiv:1706.03762"},{"key":"e_1_2_7_9_1","article-title":"Sparks of artificial general intelligence: early experiments with GPT\u20104","author":"Bubeck S","year":"2026","journal-title":"arXiv"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010023\u201002594\u2010z"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41562\u2010024\u201001991\u20109"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568\u2010024\u201000694\u20107"},{"key":"e_1_2_7_13_1","article-title":"What are the odds? 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