Potential Applications of Artificial Intelligence in Anesthesia

Dear Colleagues,

Pieces of evidence highlight the multiple potential applications of artificial intelligence (AI) and its related branches, such as machine learning (ML), in anesthesia. A key advantage of ML is the enhancement of analytic pathways based on experiences already made. Moreover, AI and ML can process multimodal data, not only numbers but also images, texts, audio files, and sounds. On these bases, several ML algorithms have been employed for improving professionals’ work and facilitating the decision-making phase. Some examples of the applications of AI in anesthesia concern the assessment of the depth of anesthesia and the control of the administration of anesthetic agents as well as muscle relaxants to realize so-called closed-loop anesthesia techniques.

Furthermore, artificial neuronal networks have been adopted to calculate the perioperative risk associated with anesthesia. The optimal management of surgical flows and the control of postoperative pain with the careful administration of opioids are further hot topics.

It is often difficult for clinicians to understand not only potential AI applications, but also the underlying mechanisms of these processes. Notably, any AI process requires the accurate identification of the most appropriate algorithms for the type of study being set up, and, above all, a quality assessment of the model. Obviously, greater confidence in the potential uses of AI could be achieved from explanations of deep learning processes. Additional issues concern data management and, above all, ethical issues.

Whatever its use, in anesthesia AI offers the opportunity to make the most of all the digital data that are produced, exploiting them to improve outcomes and optimize resources.

In the Special Issue, “Potential applications of Artificial Intelligence in Anesthesia”, original and review papers will address the latest findings on the topic, highlighting not only its clinical uses but also limits and perspectives.

Dr. Marco Cascella
Guest Editor

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• artificial intelligence
• machine learning
• computer vision
• natural language processing
• data science
• anesthesia research