Artificial intelligence (AI) is rapidly transforming the landscape of modern medicine, and anesthesiology is no exception. Traditionally a field reliant on the precision and vigilance of human practitioners, anesthesiology can embrace AI to enhance safety, efficiency, and — most importantly — the patient experience.
Separating the buzz from reality, what does AI really mean for the practice of medicine? Early evidence shows it can make a positive impact in many dimensions of healthcare, with some examples being:
- Making sense of large unstructured data sets in medical records
- Engaging in ambient listening to aid clinicians in documentation
- Using computer vision to signal and measure in a variety of clinical settings, including the operating room
- Employing machine learning to find patterns and generate predictions to power early warning systems and provide clinicians with real-time decision support
In anesthesiology this can redefine how patients experience surgery and recovery. Anesthesia is already one of the safest medical procedures; AI can help make it even safer. AI-powered systems can monitor vital signs continuously and detect subtle changes that may elude even experienced clinicians. For instance, AI algorithms are now used to predict intraoperative hypotension — dangerously low blood pressure during surgery — allowing anesthesiologists to intervene before a crisis occurs.
AI also holds the key to more personalized anesthesia care. By analyzing a patient’s medical history, functional status and even lifestyle factors, AI can help anesthesiologists better tailor drug choices and anesthesia plans to individual needs. This can improve recovery times and reduce the likelihood of complications. This is particularly beneficial for patients with complex medical histories or those undergoing high-risk procedures.
Generative AI has enabled clinical note writing and scribe systems, with the AI even making suggestions for diagnoses based on the notes it’s taking in real-time and the patient’s history. This improves the patient experience by allowing the clinicians to be “present” with their patients and focused on face-to-face conversation rather than multitasking with their computers. Moreover, generative AI tools can help translate complex medical jargon into plain language, making it easier for patients to understand their care plans. These tools can also condense hundreds of pages of medical records into summaries for both clinicians and patients.
The next level of sophistication may be agentic AI which adds the capability for adaptive and autonomous action enabling virtual assistants that help to educate patients and assist them in navigating the process before and after surgery, reducing anxiety and improving satisfaction.
AI may also fulfill the potential of long-imagined closed-loop anesthesia delivery systems, which can automatically adjust drug dosages based on real-time physiological data. These systems would maintain patients within optimal physiological ranges, reducing variability and minimizing the risk of human error. This would not only improve outcomes but also manage complex care with the highest level of precision. One such experimental system was shown to automatically make thousands of adjustments in an IV infusion medication, thereby keeping a patient’s blood pressure tightly controlled during surgery.
Anesthesia providers are often on the cutting edge of AI adoption in healthcare—learning how to adopt and harness the benefits of AI. Dr. Kelly LeBlanc of US Anesthesia Partners is one example. Dr. LeBlanc is Chief of Anesthesia at the recently opened “smart hospital,” Houston Methodist Cypress Hospital, which uses AI to enhance patient care, improve operational efficiency and optimize workflows. The hospital uses AI-powered patient monitoring in the ICU, ambient intelligence in patient rooms and ORs and real-time patient flow optimization in the emergency department.
It’s important to emphasize that AI in anesthesiology is not about replacing clinicians; rather, it’s about augmenting their capabilities. AI systems can handle routine tasks like data entry, monitoring and documentation, freeing up anesthesiologists to focus on complex decision-making and patient interaction. In this way, AI indirectly enhances the human side of medicine. For example, AI-enabled early warnings of patient risk under anesthesia can help an anesthesiologist be able to better, and more safely, supervise their operating rooms.
Of course, integrating AI into anesthesiology is not without challenges. Data privacy, algorithmic bias and the need for rigorous validation are all critical concerns. Ensuring that AI tools are transparent and subject to clinical oversight is essential to maintaining patient trust.
Moreover, the success of AI in anesthesiology depends on collaboration between technologists, clinicians and patients. Educational programs must evolve to equip anesthesiologists with the skills to interpret and manage AI tools effectively, while clinical input must be incorporated at scale to train AI tools on an ongoing basis.
By anticipating risk, personalizing care, improving communication and supporting clinicians, AI has the potential to be more than just a technology upgrade, but instead to truly transform how anesthesiology is practiced, benefitting clinicians and patients alike.
Dr. Mo Azam is the Head of Innovation for USAP. Jeff Terry is the Executive Vice President of Health System Strategy & Innovation at USAP.
