Artificial Intelligence in anesthesiology (2023-2025)

Efficacy of predictive systems for hypotension and pharmacological optimization against ethical and implementation hurdles

Abstract

Anesthesiology demands rigorous vigilance, historically based on the reactive detection of adverse events. The introduction of Artificial Intelligence (AI), encompassing Machine Learning (ML) and Deep Learning (DL) during the 2023-2025 triennium, marks a transition toward the predictive and proactive management of patient safety. The purpose of this review is to analyze the effectiveness of AI systems in predicting Intraoperative Hypotension (IOH) and optimizing drug dosage, while simultaneously examining the complex ethical, legal, and infrastructure challenges that limit their widespread adoption. Recent literature indicates that tools like the Hypotension Prediction Index (HPI) significantly reduce the burden of hypotension, while ML-enhanced closed-loop systems demonstrate greater precision and economy in the consumption of anesthetic agents such as Propofol. However, overcoming barriers such as the risk of algorithmic bias, the absence of a clear regulatory framework for civil accountability, and the need for multicenter validation and continuous training pose significant obstacles. The synthesis of evidence reaffirms the clinical utility of AI but highlights the urgency of establishing robust governance and education to ensure patient safety and equity in contemporary anesthetic practice.