Can you trust the algorithm? A clinical exploration of AI models in perioperative care
TL;DR
A narrative conceptual review of four commonly encountered AI model types: random forests, support vector machines, convolutional neural networks and large language models is presented, exploring their clinical applications, interpretability, limitations and relevance to perioperative and surgical settings.
Abstract
Artificial intelligence (AI) is increasingly influencing perioperative care by enhancing decision support, resource management, documentation, image analysis and patient safety. This article presents a narrative conceptual review of four commonly encountered AI model types: random forests, support vector machines, convolutional neural networks and large language models. It explores their clinical applications, interpretability, limitations and relevance to perioperative and surgical settings. Particular emphasis is placed on model explainability, evaluation metrics such as accuracy, F1 score and area under the receiver operating characteristic curve, hallucination risk and the challenges associated with opaque ‘black box’ systems, including non-interpretable algorithmic frameworks. The discussion is contextualised within the UK’s regulatory and governance frameworks, including the National Health Service Digital clinical safety standards, Medicine and Healthcare products Regulatory Agency requirements, National Institute for Health and Care Excellence evaluation tiers and data protection under UK General Data Protection Regulation. By improving clinician understanding of how AI systems function, fail and should be governed, this article aims to support informed, safe and ethical adoption of AI technologies in perioperative environments.