Anethesia and machine learning
Machine learning en anestesia. Avances de hoy para la anestesia del mañana
| dc.creator | Núñez, Agustín | |
| dc.creator | Tawfiq, Samer | |
| dc.creator | Polit, Andrés | |
| dc.date | 2024-04-10T01:46:45Z | |
| dc.date | 2024-04-10T01:46:45Z | |
| dc.date | 2023 | |
| dc.date.accessioned | 2024-07-17T21:15:16Z | |
| dc.date.available | 2024-07-17T21:15:16Z | |
| dc.identifier | 10.25237/revchilanestv52n6-04 | |
| dc.identifier | 07164076 | |
| dc.identifier | https://hdl.handle.net/20.500.12728/10685 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9509823 | |
| dc.description | Artificial intelligence (AI) is concerned with developing systems that perform tasks that typically require human intelligence. Machine learning (ML) is an important branch of AI and has significant applications in medicine. These applications have allowed advancements in anesthesiology, where algorithms capable of recognizing patterns in arterial waveforms and predicting episodes of hypotension have been developed, reducing postoperative pain and monitoring anesthesia. All of these tools are capable of assisting physicians in event prevention and decision-making. However, it is important to note that, up to now, ML-based tools cannot replace the clinical judgment of an anesthesiologist due to potential biases inherent in initial programming. © 2023 Sociedad de Anestesiologia de Chile. All rights reserved. | |
| dc.format | application/pdf | |
| dc.language | es | |
| dc.publisher | Sociedad de Anestesiologia de Chile | |
| dc.subject | anesthesia | |
| dc.subject | Artificial intelligence | |
| dc.subject | intraoperative complications | |
| dc.subject | intraoperative monitoring | |
| dc.subject | machine learning | |
| dc.title | Anethesia and machine learning | |
| dc.title | Machine learning en anestesia. Avances de hoy para la anestesia del mañana | |
| dc.type | Article |