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麻醉学中的人工智能和机器学习 - PubMed
Source: pubmed.ncbi.nlm.nih.gov
Article summary:
1. Artificial intelligence and machine learning have recently made significant progress in commercial applications, particularly in image recognition, natural language processing, language translation, text analysis, and self-learning.
2. Anesthesiology practice requires high reliability and pressure cycles for interpretation, physical action, and response rather than any single cognitive behavior.
3. This review covers the basics of artificial intelligence and machine learning for practicing anesthesiologists, describing how decision-making emerges from simple equations.
Article analysis:
The article is generally reliable and trustworthy as it provides a comprehensive overview of the use of artificial intelligence (AI) and machine learning (ML) in anesthesiology. The author has provided a clear explanation of the concepts involved with AI/ML as well as their potential applications in anesthesiology. The article also includes examples to illustrate the points made throughout the text.
The article does not appear to be biased or one-sided in its reporting; it presents both sides of the argument fairly by discussing both the potential benefits and risks associated with AI/ML in anesthesiology. It also acknowledges that AI/ML may not be suitable for all clinical scenarios due to its reliance on data accuracy and availability.
The article does not appear to contain any unsupported claims or missing points of consideration; all claims are supported by evidence from relevant sources such as research studies or other publications. Furthermore, all potential risks associated with AI/ML are noted throughout the text.
In conclusion, this article is reliable and trustworthy due to its comprehensive coverage of AI/ML in anesthesiology as well as its balanced presentation of both sides of the argument without any unsupported claims or missing points of consideration.