Large language models (LLMs) are moving from proof-of-concept to practice-ready tools in anesthesiology. In 2022, a BERT-driven network predicted 30-day postoperative mortality by reading narrative procedure text, showing that risk stratification can be enhanced by free-text in addition to structured variables. One year later, whole-note ASA-PS classification demonstrated that BioClinicalBERT and fastText translate pre-operative narratives into illness-severity scores, while token-level Shapley maps exposed the clinical cues behind each decision—proving that interpretability and accuracy can coexist. Work published in 2024 broadened the horizon: GPT-4 Turbo, guided by few-shot and chain-of-thought prompting, produced natural-language justifications for multiple peri-operative outcomes, matching specialist models on categorical tasks yet struggling with continuous metrics such as length of stay-highlighting the impact of data type on LLM reasoning. Most recently, a retrieval-augmented LLM embedded textbook passages into its inference loop. Grounded predictions improved sensitivity in rare, high-risk cohorts and delivered fully traceable rationales, shifting the narrative from "black-box" to "evidence-seeking collaborator". Together, these studies mark three conceptual milestones: (1) free-text risk stratification, (2) explainability through token-level or chain-of-thought reasoning, and (3) retrieval grounding to enhance trust and mitigate hallucinations. Upcoming challenges include fusing structured and unstructured data, modeling time-series continuous variables, and navigating governance for safe clinical deployment. The lecture will map these advances into a pragmatic roadmap for integrating LLMs as reliable co-pilots in perioperative care.