The Role of Artificial Intelligence in Predicting Anesthetic Complications: A New Frontier in Patient Safety

This study explores the potential of AI-driven predictive models to enhance the identification of anesthetic complications compared to traditional clinical assessments, aiming to improve patient safety in perioperative care. Five AI systems—ChatGPT, Gemini, PubMedBERT, ClinicalBERT, and BioGPT—were evaluated using simulated patient data derived from publicly available, anonymized datasets, including synthetic genomic profiles. AI-generated risk assessments were compared to traditional methods in three hypothetical clinical scenarios. Results showed that AI models predicted complications, such as hypotension and respiratory failure, with superior accuracy (AUC 0.92 vs. 0.78) and reduced simulated opioid consumption by 30%. Challenges include data reliability and algorithmic bias. AI demonstrates significant promise as a decision-support tool but must complement clinical expertise.