From prediction to practice: closing the translation gap in artificial intelligence for anesthesia

Comprehensive Summary

As advancements in artificial intelligence (AI) continue to emerge in medicine, there still exists a gap between the promising potential of AI in the field of anesthesiology and its actual application in the clinical setting. This review addresses the causes of this translational gap in order to provide practical solutions to support the integration of AI in anesthesiology. It is often the case that contemporary AI models are trained on homogenous datasets that overlook rare patient presentations, but these are the clinical scenarios in which accuracy becomes crucial. Despite growing findings in the applicability of AI in anesthesia, there still exists a lack of robust, large-scale studies to demonstrate clinical relevance. This paired with potential ambiguity when it comes to understanding how AI models operate could reduce anesthesia providers' confidence in using these tools to aid in clinical decision making. Strategies to bridge this translational gap include conducting rigorous prospective studies to validate the practicality of AI tools in anesthesia delivery. Collaboration across disciplines should be encouraged to seamlessly integrate AI into existing clinical workflow. The future of AI in anesthesia is projected to involve proactively incorporating AI tools into anesthesia platforms and using AI to combine sources of information from various data collecting methods to provide patient-centered anesthesia.

Outcomes and Implications

Potential implications of bridging the gap between AI and anesthesiology could include exacerbating alarm fatigue and increasing demands on organizations' existing technical systems. Privacy errors can result from potential data breaches if the AI tools are storing sensitive patient information for review. Issues with liability when it comes to interpreting who is responsible for medical errors in AI-assisted decision making is a serious ethical implication to consider. Further questions about the business model with regard to reimbursing anesthesia care supported by AI technology are other barriers that would need to be discussed before AI can successfully integrate into the field of anesthesiology.