Address correspondence to James Harvey Jones, MD, Department of Anesthesiology, N2198 UNC Hospitals, CB #7010, Chapel Hill, NC 27599. Address e-mail to [email protected].

Preoperative optimization clinics enhance surgical outcomes by optimizing patients' health, reducing unnecessary tests, and minimizing cancellations. Artificial intelligence (AI) now promises to further advance these efforts by improving predictive modeling, automating risk assessments, and enabling personalized care strategies. AI models have shown particular strength in predicting postoperative complications, such as cardiovascular events. Tools like natural language processing (NLP) have also improved risk detection for conditions like alcohol misuse. The historical development of preoperative clinics -- from Dr. J Alfred Lee's initial concept in 1949 to modern models like Enhanced Recovery After Surgery (ERAS) and Anesthesia Preoperative Evaluation Clinic (APEC) -- laid the groundwork for today's integration of electronic health records (EHRs) and decision-support systems, now evolving toward AI-driven care. Machine-learning algorithms have proven superior to traditional models for predicting postoperative anemia, opioid dependence, diabetes complications, and mortality risks, thus offering precise stratification and resource optimization. Specific applications include AI-assisted anemia management, penicillin allergy delabelling, and opioid use prediction after major surgeries. AI also enhances patient education through tools like ChatGPT and improves smoking cessation efforts using conversational AI. NLP has demonstrated better accuracy than standard International Classification of Disease (ICD) codes in identifying risky alcohol use (>2 standard drinks per day before surgery). However, barriers to widespread adoption include data privacy, algorithmic bias, and clinician skepticism. Future studies should focus on validating AI models across diverse populations and integrating AI recommendations within clinical workflows while adhering to evolving regulatory standards. Ultimately, AI's incorporation into preoperative assessments could significantly boost the efficiency and impact of clinic resources, potentially shifting outcomes more favorably by improving the slope of resource utilization versus patient outcome curves. Continued research and refinement are essential for AI to achieve its full potential in perioperative medicine.