I shows promise in anesthesiology, but experts agree that leveraging the technology effectively requires a collaborative approach to ensure patient safety.
Anesthesiology is well-positioned to benefit from advances in artificial intelligence as it touches on multiple elements of clinical care, including perioperative and intensive care, pain management, and drug delivery and discovery.
As the development and application of artificial intelligence technologies in medicine continue to grow, it is important for clinicians in every field to understand what these technologies are and how they can be leveraged to deliver safer, more efficient, and more cost-effective care.
Anesthesiology is well-positioned to benefit from advances in artificial intelligence as it touches on multiple elements of clinical care, including perioperative and intensive care, pain management, and drug delivery and discovery.
Traditional computer programs are programmed with explicit instructions to elicit certain behaviors from a machine based on specific inputs. Machine learning, on the other hand, allows programs to learn from and react to data without explicit programming. Data that can be analyzed through machine learning are broad and include, but are not limited to, numerical data, images, text, and speech or sound.
AI applications
There are several areas in which AI plays a significant role in anesthesiology. Some of them are mentioned below:
- Depth of anesthesia monitoring:
Machine learning approaches are well-suited to analyze complex data streams such as electroencephalography's; thus, a range of electroencephalography-based signals was found to have been investigated to measure the depth of anesthesia.
- Control of anesthesia delivery:
As automated delivery of anesthesia also requires a machine's determination of the depth of anesthesia, approaches to control require the measurement of clinical signs or surrogate markers of anesthetic depth. Thus, the evolution of control system research in anesthesia is evident in the various targets used to approximate the depth of anesthesia.
- Event prediction:
For perioperative care risk prediction, various techniques in machine learning, neural networks, and fuzzy logic have all been applied.
- Ultrasound guidance:
In addition to specific structure detection in ultrasound images, researchers have also used neural networks to assist in identifying vertebral levels and other anatomical landmarks for epidural placement.
- Pain management:
Machine Learning can analyze differences in functional magnetic resonance imaging data collected from human volunteers exposed to painful and nonpainful thermal stimuli, demonstrating that machine learning analysis of whole brain scans could more accurately identify pain than analysis of individual brain regions traditionally associated with nociception.
- Operating room logistics:
Fuzzy logic and neural networks were used to optimize bed use for patients undergoing ophthalmologic surgery by modeling the type of case, modeling surgeon experience, staff experience, type of anesthesia and the experience of the anesthesiologist, patient factors, and comorbidities with error rates ranging from 14% to 19% depending on the type of case.
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