El-Hassoun O, Maruscakova L, Valaskova Z, Bucova M, Polak S, Hulin I
Bratisl Lek Listy. 2019;120(3):218-222. doi: 10.4149/BLL_2019_028.
The race to make the dream of artificial intelligence a reality comes parallel with the increasing struggle of health care systems to cope with information overload and translational pressure. It is clear that a shift in the way data is generated requires a shift in the way they are processed. This is where AI comes with great promises to solve the problem of volume versus applicability of information in science. In medicine, AI is showing exponential progress in the fields of predictive analysis and image recognition. These promises however, come with an intricate package of ethico-social, scientific and economic implications, towards which a reductionist approach leads to distorted and dramatic predictions. All this, in a time when the growing pressure on healthcare systems towards defensive medicine begs the question of the true need for AI for good medical practice.This article examines the concept and achievements of AI and attempts to offer a complex view on the realistic expectations from it in medicine, in the context of current practice (Ref. 38). Keywords: algorithms, artificial intelligence, image recognition, neural networks, predictive analysis.
让人工智能梦想成为现实的竞赛,与医疗保健系统应对信息过载和转化压力的日益艰难同步进行。显然,数据生成方式的转变需要数据处理方式的转变。这正是人工智能有望解决科学领域中信息数量与适用性问题的地方。在医学领域,人工智能在预测分析和图像识别方面正呈现出指数级进展。然而,这些前景伴随着一系列复杂的伦理社会、科学和经济影响,采用简化论方法会导致扭曲且夸张的预测。而在此时,医疗保健系统面临的防御性医疗压力不断增加,这引发了对于良好医疗实践中人工智能真正需求的质疑。本文探讨了人工智能的概念与成就,并尝试在当前实践背景下,对医学中对其的现实期望提供一个全面的观点(参考文献38)。关键词:算法、人工智能、图像识别、神经网络、预测分析。