Clinical Applications of AI and Machine Learning in Anesthesiology

In the preoperative phase, AI algorithms have been applied to predict patient outcomes, assess surgical risks, and optimize surgical planning (Ann Surg 2018;268:70-6). Machine learning models analyze patient data to identify patterns and relationships that guide preoperative decision-making. This information allows for better resource allocation and tailored interventions to reduce risks and improve patient outcomes.

AI is revolutionizing personalized medicine in perioperative care and anesthesiology by creating custom treatment plans based on each patient’s distinct characteristics and needs.

AI contributes to perioperative care through predictive analytics, which examines extensive patient data to identify preoperative risk assessments, surgical planning, and postoperative care management. AI can recognize patients with a high risk of complications after surgery, allowing clinicians to create targeted interventions and optimize treatment plans. For example, AI can evaluate a patient’s medical history, lab results, and other relevant factors to develop a personalized preoperative preparation plan that minimizes potential risks (JAMA Netw Open 2023;6:e2322285).

AI has been employed to monitor patients for complications and to predict the likelihood of adverse events. Machine learning algorithms analyze physiological data to predict the risk of complications, enabling health care providers to intervene in time and reduce risk (Curr Genomics 2021;22:291-300).

AI and machine learning improve anesthesia monitoring during surgery using EEG signals (Med Sci Monit 2023;29:e938835; IEEE J Biomed Health Inform 2018;22:671-7). AI closed-loop systems using multiparameter platforms, including BIS, have been created for precise titration of anesthesia and analgesia (Anesthesiol Clin 2021;39:565-81). AI enhances control by using machine learning with BIS as a target measure and to automate neuromuscular blockade and drug pharmacokinetics (Anesthesiology 2020;132:379-94). AI is also used to control mechanical ventilation and to automate weaning (Int J Environ Res Public Health 2021;18:2713).

Using multiparameter platforms, including EMR, to create point-of-care guidance and accurate predictions about risk factors and potential complications leads to optimization of monitoring, medication usage, and preemptive management. Such platforms streamline early diagnosis, appropriate patient-specific treatment, and prevention of a disease state.

AI models were developed for electroencephalography analysis allowing anesthetic titration (Saudi J Anaesth 2022;16:86-93). Decision support systems were also developed for timely and effective treatment, such as AI-powered fluid and inotrope goal-directed therapy (Anesthesiology 2020;133:1214-22). AI also aids in prediction of intraoperative hypotension up to 15 minutes prior to an event, with a sensitivity of 88% and specificity of 87%, and prediction of acute kidney injury by monitoring intra-abdominal pressure and urine output (Anesthesiology 2020;133:1214-22).

AI can enhance decision-making in perioperative care by analyzing large volumes of patient data, leading to better outcomes and reduced costs (JAMA Surg 2021;156:941). AI-powered decision support tools can assist clinicians in making more accurate diagnoses and treatment decisions, reducing the likelihood of medical errors. Preventing medical errors can result in significant cost savings for health care providers and improved patient outcomes, highlighting the economic benefits of AI in perioperative care (J Med Internet Res 2020;22:e16866; BJA Educ 2023;23:288-94).

Neural networks are the most commonly employed method of achieving ultrasound image classification. Studies find that deep learning greatly improves the accuracy of the images (Engineering 2019;5:261-75). It enables health care professionals to perform high-quality ultrasound scanning through AI guidance by screening and image-capture in real time along with automatically optimizing image quality and calculating cardiac parameters such as ejection fraction, stroke volume, cardiac output, and systolic cardiac function (J Imaging 2023;9:50). Machine learning models use big data to combine echocardiography measurements and EMR information to predict outcomes and early diagnosis and management. AI deep learning echocardiography modules accurately identify cardiac structure and function, including left ventricular hypertrophy, left ventricular end-diastolic volume, and ejection fraction.

AI in pain management uses machine learning to analyze whole brain scans, estimate opioid dosing, identify patients for preoperative consultation, and analyze EEG signals to predict response to opioid therapy for acute pain (65% accuracy) (Ann Transl Med 2022;10:528; Eur J Pain 2017;21:264-77). AI smart, connected, wearable, and implanted devices can automatically adapt and anticipate patients’ neuromodulation and pain therapy needs (closed-loop). This can significantly reduce the need for prescription opioids, reduce hospital admissions due to overdose, and help to address the nationwide opioid crisis. AI systems are used in real time to identify anatomical structures and provide needle trajectory guidance for regional anesthesia and pain management blocks (J Healthc Eng 2021;2021:6231116).

AI optimizes OR logistics by analyzing scheduling and tracking movements and actions of anesthesiologists. AI approaches optimize bed use for surgery patients, improving organization and logistics to reduce waste and optimize time and manpower, leading to reduced health care costs. Digital connectivity and interoperability, smart sensors, and devices can improve communication, functionality, safety, quality, workflow efficiency, and productivity. AI automates surgical and pharmaceutical inventory management and charge capture. The AI platform is used to optimize OR utilization, throughput, and bed management. Studies have shown that machine learning platforms optimized EMR data captured from clinicians’ notes, accurately predicted ICD-10 diagnosis codes, and improved revenue cycle management (Big Data and Cognitive Computing 2022;6:76; Anesth Analg 2023;136:637-45).

Workflow optimization and resource allocation have also benefited from AI applications. Machine learning algorithms predict patient flow, allowing for better surgical scheduling and more efficient resource usage. Resource allocation can also be improved by using AI-powered tools to optimize surgery scheduling and distribution of resources like ORs and staff, increasing efficiency and reducing costs (J Med Internet Res 2020;22:e16866; Anesth Analg 2023;136:637-45).

Furthermore, AI assists in personalizing care pathways by tailoring interventions to individual patient needs and preferences. Personalized care enabled by AI allows health care providers to allocate resources effectively and prioritize patients based on individual needs, reducing the overall cost of care (J Med Internet Res 2020;22:e16866). AI-driven postoperative monitoring can identify complications early, potentially reducing hospital stays and associated costs.

Risk stratification can help tailor anesthesia care to an individual patient, reducing patient-specific variability. AI-based point-of-care decision support systems can support evidence-based clinical practice and improve patient care. AI risk models and algorithms in perioperative care use neural networks and machine learning to predict the following: hypnotic effect, return of consciousness, neuromuscular block, hypotension, patient alertness, laryngoscopy findings, breathing, optimal anesthesia method, morbidity, deterioration, mortality, readmission, sepsis, airway difficulty, cardiac (including heart failure) and pulmonary risk (such as pneumonia, respiratory failure, hypoxemia, and reintubation), nausea and vomiting, postoperative bleeding, acute kidney injury, and delirium risk (Anesthesiology 2020;132:379-94; Ann Transl Med 2022;10:528).

AI can enhance clinical trial efficiency by optimizing patient recruitment, data analysis, and monitoring, reducing the time and cost of bringing new treatments to market (J Med Internet Res 2020;22:e16866). Expediting new therapies and medical device adoption ultimately benefits patients and health care providers.

AI can identify best practices and care pathways for optimal perioperative outcomes, develop cost efficiency models and data analysis to identify risk factors, and prevent future medical errors.

The future direction of AI in perioperative care and anesthesiology is expected to address many of the current challenges while also introducing new opportunities for improved patient outcomes and more efficient care delivery. Integrating AI in perioperative care and anesthesiology has immense potential to revolutionize the field. AI’s role is becoming increasingly integral to successful patient outcomes, from enhancing preoperative planning to facilitating intraoperative decision-making, improving postoperative care, and reducing overall costs (Anesth Analg 2023;136:637-45).

In the current discourse surrounding AI, it is crucial to delineate the various limitations that challenge its applicability across health care and its ethical deployment. Foremost among these is the issue of generalizability; while AI models often excel in specialized tasks, their performance notably degrades when confronted with conditions or variables divergent from their training sets. This challenge is exacerbated by reproducibility concerns, as the frequent reliance on proprietary algorithms, confidential datasets, and often undisclosed hyperparameters impedes the ability to replicate or validate research findings, stalling scientific progress. Although AI has been prolifically adopted in health care, further empirical studies are needed to confirm improvements in patient outcomes and validation of generalized practical utility. Ethical dilemmas add another layer of complexity; data privacy issues and the risk of perpetuating societal biases through algorithmic decision-making necessitate rigorous ethical review. Complicating matters further are the significant computational demands of state-of-the-art models, which require substantial financial investments. The issue of interpretability, commonly called the “black box problem,” hampers the elucidation of algorithmic decision-making processes, presenting a critical limitation in contexts within the high levels of accountability mandated in medicine. These multifaceted limitations warrant a nuanced and multidisciplinary approach to AI research and development that actively addresses the challenges of facilitating responsible and practical advancements in the field (Artificial Intelligence in Healthcare. 2020).