Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications
Abstract
:1. Introduction
2. AI in Anesthesia
3. Ethical Implications
Scientific Output, Ongoing Research, and Perspectives
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Application | Strategy | [Refs.] |
---|---|---|
Presurgical Evaluation | Preoperative management and planning. | [9,14] |
Anesthetic Dosage Optimization | AI algorithms can analyze a patient’s medical history, physiological data, and other factors to determine the most appropriate anesthetic dose, which can improve patient safety and minimize complications. | [6,15] |
Preventing Drug Errors | AI-powered systems can help prevent drug administration errors by verifying medication orders, drug interactions, and dosages. | [16,17] |
Early Detection of Complications | AI can be used to analyze various data streams in real time to detect patterns or anomalies that may indicate a potential complication, such as hypoxia and hypotension. | [4,18] |
Predicting Patient Outcomes | Machine learning algorithms can be used to predict the likelihood of complications or adverse outcomes during surgery, allowing anesthesiologists to make more informed decisions about patient care. | [12] |
Predicting Postsurgical ICU Admission | Various machine learning algorithms can be employed to develop predictive models from data, such as patient demographics, medical history, surgical procedures, vital signs, laboratory values, and other clinical factors. | [13] |
Automated Monitoring | AI-powered monitoring systems can continuously track a patient’s vital signs, such as heart rate, blood pressure, and oxygen saturation, and alert the anesthesiologist to any potential problems. | [18] |
Automated Anesthesia Dosing | AI can help tailor anesthesia delivery and optimize closed-loop systems based on patient data. | [4,6] |
Pain Management | AI can help adjust the dosage and administering of drugs to achieve optimal postoperative pain control. | [19,20] |
Real-Time Decision Support | AI algorithms can provide real-time decision support to anesthesiologists during surgery, such as recommending alternative drug options if a patient is not responding to the initial anesthetic. | [21] |
Postoperative Monitoring | AI can help monitor patients postoperatively, predicting and detecting any adverse events that might occur, and giving alerts and recommendations to physicians. | [22] |
Resource Allocation | AI algorithms can help optimize the use of anesthesia resources by identifying the best candidates for specific procedures and reducing unnecessary anesthesia use. | [23] |
Training and Education | AI can be used to simulate scenarios and train anesthesiologists to handle challenging situations, such as unexpected complications during surgery. | [24,25] |
Image Recognition | AI algorithms can analyze medical images to identify anatomical structures and guide the placement of regional anesthesia techniques. | [8] |
Research and Analysis | AI can help analyze vast amounts of data from anesthesia records, patient charts, and other sources to identify patterns, trends, and insights that could help improve patient outcomes and safety. | [26,27] |
Issue | Notes | [Refs.] |
---|---|---|
Bias and discrimination | AI systems may perpetuate biases and discrimination if they are trained on biased data. | [37,38,39] |
Privacy and security | It is mandatory to establish robust security protocols. | [41,42,43,44,45,46] |
Accountability and transparency | Model explainability is the ability of AI and machine learning models to provide clear and interpretable explanations of their decision-making process. | [47,48] |
Access to healthcare | AI systems may exacerbate existing disparities in access to healthcare, as the use of AI may require expensive technology or specialized training that is not available to all healthcare providers. | [49] |
Regulatory processes | Governments and other stakeholders must define technical standards for ensuring the accuracy, reliability, and safety of AI systems, as well as guidelines for the ethical use of AI. | [40] |
Biorepositories | Ethical guidelines and protocols, as well as uniform methodological approaches and international standards, are required. | [43,44,45,46] |
Ethicists involvement | Ethicists should be involved in AI projects from the beginning. | [50,51] |
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Cascella, M.; Tracey, M.C.; Petrucci, E.; Bignami, E.G. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. Surgeries 2023, 4, 264-274. https://doi.org/10.3390/surgeries4020027
Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. Surgeries. 2023; 4(2):264-274. https://doi.org/10.3390/surgeries4020027
Chicago/Turabian StyleCascella, Marco, Maura C. Tracey, Emiliano Petrucci, and Elena Giovanna Bignami. 2023. "Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications" Surgeries 4, no. 2: 264-274. https://doi.org/10.3390/surgeries4020027