Publications by authors named "Roberto Lanza"
New technologies in anesthesia and intensive care: take your ticket for the future.
J Anesth Analg Crit Care
May 2023
The modern world runs all around hi-tech, which surrounds us in our everyday life. The medical field is no less; the introduction of the novel disruptive technologies are transforming every healthcare system. Anesthesia, intensive care, and pain medicine are fields in which the application of new technologies is proving to have great potential.
View Article and Find Full Text PDFArtificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted.
View Article and Find Full Text PDFArticle Synopsis
- - AI serves as a valuable asset for researchers and clinicians, but it requires careful use due to various potential risks and challenges in its application.
- - The analogy of PAC-MAN highlights how researchers are like the game's character, constantly pursuing data while maneuvering through obstacles that symbolize ethical concerns, poor-quality data, and legal complexities.
- - To effectively harness AI, clinical researchers must diligently gather and analyze data, navigating complex challenges to ensure the accuracy and relevance of their findings.
Article Synopsis
- Artificial intelligence (AI) aims to mimic human brain processes to help computers learn and control environments, particularly in fields like anesthesiology and intensive care by analyzing large data sets in real-time for better clinical decision-making.
- Machine Learning, a subfield of AI, uses algorithms that improve autonomously by identifying patterns in data, enhancing diagnosis, prognosis, and treatment strategies.
- Effective application of machine learning requires high performance and quality control measures throughout various stages, from task definition to model evaluation, making it easier for less experienced users with specific tools for data engineering and analytics.