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(A)nesthes(I)ology: An Overview of AI in Anesthesia

by Jake Sebree, University of Cincinnati College of Medicine

It is no secret that artificial intelligence (AI) has been making massive leaps in recent years, especially with the mainstream attention generative AI like ChatGPT has been receiving. While it is undeniably impressive to have programs that can craft jokes, generate creative recipes, and help write that overdue email that’s been sitting in your drafts folder for a week, the implications of AI stretch far beyond these conveniences. As future anesthesiologists, we must consider how similar technologies are transforming the field and how we can leverage them to improve patient care and safety. AI’s ability to analyze vast amounts of data, identify patterns, and make predictions has positioned it as a valuable tool in various aspects of anesthesia practice. This article aims to provide a brief introduction to AI in anesthesiology, tailored for medical students who are exploring the field.

Some of the most prevalent areas of development of AI in anesthesiology are in monitoring and decision support. AI and machine learning (ML) systems can enhance real-time patient monitoring by analyzing vital signs, physiological parameters, and anesthetic drug levels, which provide an added level of support to anesthesia providers with the intent of increasing efficiency and addressing challenges such as monitoring overload and alarm fatigue.(1) These tools also have the capability to enable predictive analytics to identify complications early, allowing clinicians to act proactively instead of reactively to adverse events. 

Beyond monitoring, AI is being actively studied for their potential to improve outcomes in pain management and regional anesthesia.(2) AI algorithms are being developed to provide real-time guidance for needle placement during regional anesthesia and vascular access, using ultrasound overlay to help identify anatomy during image guided interventions. These AI-driven assessment tools analyze both subjective input from the practitioners and objective data to create tailored management strategies, improving patient comfort and outcomes. These advancements in image-guided planning represent the cutting edge of technological integration into anesthesiology to enhance procedural precision.

The integration of AI in anesthesia will not be met without its challenges. To start, there are ethical considerations that must be addressed to ensure safe and equitable use in clinical settings. Key concerns include biases in AI models that can potentially lead to unfair outcomes for underrepresented groups.(3) Privacy and security of patient data are also important issues to consider, as ML often relies on large datasets for training. This raises concerns about patient consent and data breaches, both of which should be high priority in protecting as new technology develops.(4) Additionally, as AI is implemented into the decision-making processes, clinicians need to be conscious not to foster over-reliance on these systems. We do not want to regress in the human expertise and skill because of new technology. 

This wouldn’t be a discussion of AI without the topic of job displacement. However, AI in anesthesia is a great example of how the technology can augment over automate.(5) AI will never replace the human connection between patient and provider. Balancing AI's benefits with the need for human expertise, that is essential in anesthetic care, ensures we enhances the field rather than undermine it.

As future providers, it will be our responsibility to integrate new technologies for the benefit of patient care and safety. It will also be important to approach these advancements with both enthusiasm and critical thinking. By actively participating in the adoption and development of AI, we can help shape a future where technology serves as an ally in improving outcomes, reducing disparities, and enhancing the overall patient experience. Ultimately, while the tools we use will change, the essence of what we do—providing compassionate, safe, and effective care—will remain constant throughout our practice.

References 
1. Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth. 2024 Apr-Jun;18(2):249-256. doi: 10.4103/sja.sja_955_23. Epub 2024 Mar 14. PMID: 38654854; PMCID: PMC11033896.
2. Karmakar A, Khan MJ, Abdul-Rahman ME, Shahid U. The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments. Cureus. 2023 Aug 29;15(8):e44306. doi: 10.7759/cureus.44306. PMID: 37779803; PMCID: PMC10535025.
3. Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health. 2019 Dec;9(2):010318. doi: 10.7189/jogh.09.020318. PMID: 31788229; PMCID: PMC6875681.
4. Cascella, Marco & Tracey, Maura & Petrucci, Emiliano & Bignami, Elena. (2023). Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. Surgeries. 4. 264-274. 10.3390/surgeries4020027.
5. Bellini V, Rafano Carnà E, Russo M, Di Vincenzo F, Berghenti M, Baciarello M, Bignami E. Artificial intelligence and anesthesia: a narrative review. Ann Transl Med. 2022 May;10(9):528. doi: 10.21037/atm-21-7031. PMID: 35928743; PMCID: PMC9347047.

Date of last update: February 7, 2025