The integration of artificial intelligence (AI) into healthcare represents a paradigm shift in both clinical practice and administrative operations. This narrative review examines the transformative role of large language models (LLMs) and generative AI across two intersecting domains. Firstly, clinical applications, including early disease detection, medical imaging, personalized medicine, and chronic disease monitoring. Secondly, administrative functions, encompassing automated documentation, electronic health record (EHR) enhancement, conversational agents, and patient flow optimization. Through a health informatics lens, oncology is used as an illustrative setting, rather than a formal case study, to examine how AI systems engage with clinical reasoning, workflow integration, and enterprise-scale deployment. The trajectory from IBM Watson for Oncology to contemporary platforms such as Tempus and PathAI reflects both the progress achieved and the persistent challenges of scalability, interpretability, and equitable data representation. Emerging LLM-based tools, including ambient documentation systems, have been reported to reduce administrative burden in some studies, though the evidence base remains nascent and context-dependent. Critical limitations are identified across ethical, regulatory, and practical dimensions. Some of these limitations include algorithmic opacity, automation bias, dataset inequity, and the risk that automated note generation may attenuate the cognitive rigor inherent to clinical documentation. Future directions emphasize the development of multimodal and human-centered AI systems evaluated through external and prospective validation and reported in line with guidelines like CONSORT-AI and SPIRIT-AI, alongside robust post-deployment monitoring. Ultimately, oncology’s experience illustrates potentially transferable principles for responsible AI adoption across healthcare specialties, suggesting that sustainable integration depends not solely on technical performance, but on transparency, governance, and meaningful clinician–AI collaboration.