RESUMEN
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. However, it is crucial that this digital medical transformation always takes place under the coordination of natural (human) intelligence.
RESUMEN
Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by "ghosts", which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.
RESUMEN
Artificial 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. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.
Asunto(s)
Anestesiología , Inteligencia Artificial , HumanosRESUMEN
BACKGROUND AND AIM: Artificial intelligence was born to allow computers to learn and control their environment, trying to imitate the human brain structure by simulating its biological evolution. Artificial intelligence makes it possible to analyze large amounts of data (big data) in real-time, providing forecasts that can support the clinician's decisions. This scenario can include diagnosis, prognosis, and treatment in anesthesiology, intensive care medicine, and pain medicine. Machine Learning is a subcategory of AI. It is based on algorithms trained for decisions making that automatically learn and recognize patterns from data. This article aims to offer an overview of the potential application of AI in anesthesiology and analyzes the operating principles of machine learning Every Machine Learning pathway starts from task definition and ends in model application. CONCLUSIONS: High-performance characteristics and strict quality controls are needed during its progress. During this process, different measures can be identified (pre-processing, exploratory data analysis, model selection, model processing and evaluation). For inexperienced operators, the process can be facilitated by ad hoc tools for data engineering, machine learning, and analytics.