Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

Abstract

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

A European roadmap to a digital epidemiology in public health system

“…Investigations of the burden of disease taught us already the need of digital epidemiology tools at the community, national, international, and global scale. Since then, artificial intelligence and data sharing technologies have already been established in clinical medicine (12,13). Digital epidemiology is thus mandatory for the public health service.…”

Critical Review on the Contribution of Machine Learning to Health Science

“…Many ML models, particularly deep learning algorithms, are often considered "black boxes," making it difficult to understand the reasoning behind their predictions. This can hinder trust and acceptance among healthcare providers 13,14 .…”

“…The success of ML models is heavily dependent on the quality and availability of healthcare data, which can be limited or biased in certain settings 13,14 . Insufficient or biased data can lead to poor model performance 14 .…”

“…3. Ethical and regulatory concerns: The use of ML in healthcare raises important ethical and regulatory considerations, such as patient privacy, data security, and algorithmic bias, which need to carefully addressed 13,14 .…”

“…The literature review found that only a small percentage of studies reported external validation of their ML models, which is crucial for assessing their realworld performance and generalizability 13 .…”