Large language models in anaesthesiology: Current insights and future directions– A narrative review

• Author Details:   
• Lalit Gupta ^*
• Devang Bharti
• Saurabh Taneja

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Large Language Models (LLMs) promise to revolutionize the field of anaesthesiology as they have the potential to solve major challenges in this high-stakes field requiring quick decisions with utmost reliance on data. LLMs can facilitate enhanced clinical workflows for improving patient safety and outcomes through real-time decision support, risk prediction, and patient monitoring during anaesthesia and surgery. They can also provide an alternative documentation route for streamlining the administrative burden. Another useful advantage is the ability to provide multilingual education to the patients, thus improving the efficiency and accessibility of healthcare. In training, LLMs can assume the role of simulating complicated scenarios, thus accelerating anaesthesiologists’ learning and reasoning skills in critical events. Concerns regarding the ethics, biases, and possible over-reliance on AI represent a challenge that needs to be handled. It is the effective balance of this artificial power with proper human oversight that will ensure safety and trust. Thus, LLMs are a promising tool in the future vision to improve anaesthesia practice, enhance workflows, enable accurate care, and work for under-resourced areas. However, validation, transparency, legal and technical policies must support LLM use so that they complement rather than displace clinical judgment.

Keywords: Large language models (LLMs), Anaesthesiology, Artificial intelligence (AI), Machine learning in perioperative medicine, Natural language processing (NLP) in Anaesthesia, Automated documentation in anaesthesia practice, Clinical decision support.