AI Article Synopsis
- The integration of AI and ML in anesthesiology and perioperative medicine is transforming clinical practices by synthesizing patient data for better precision medicine and predictive assessments.
- Emerging AI models help in various areas such as assessing anesthetic depth, predicting surgical events and risks, enhancing ultrasound guidance, managing pain, and optimizing operating room logistics.
- The analysis of these technologies aims to evaluate their strengths, weaknesses, opportunities, and threats (SWOT) to guide the future adoption of AI models in anesthesiology workflows.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10912557 | PMC |
| http://dx.doi.org/10.3389/fdgth.2024.1316931 | DOI Listing |
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