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The applications of artificial intelligence (AI) and machine learning (ML) have shown promising results in healthcare. However, while many advances have been made to incorporate AI into the field of anesthesiology since it was first used to automate anesthetic delivery, it is still not commonplace. Previous studies have demonstrated that ML algorithms are useful in perioperative management, and the contributions of AI to general anesthesia have yielded advancements in closed-loop systems. Although these tools may ultimately help anesthesiologists guide clinical decision making, it is still unknown how ML-based predictions should be managed in real-time. The fields of postoperative pain management and chronic pain have benefited from AI by developing software capable of predicting pain level and analgesia response, allowing for increasingly individualized care. Importantly, data amalgamation and ML techniques may not solely be useful in direct patient care, but will also increase the training power of simulations by providing high fidelity clinical scenarios and unbiased feedback, thereby improving education in anesthesiology. It is clear that AI will find many applications in anesthesia care, in delivering real- time results and patient assessments to enable physicians to focus on higher-order tasks. However, much more work is required to understand exactly the scope that AI will play in anesthesiology.