Artificial intelligence—Developments in medicine in the last two years

Abstract

Dear Editor, Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. There are some knowledge and thinking tasks that humans cannot perform as perfectly as they wish to or should be able to. These tasks are closely related to security and responsibility. A multitude of cognitive distortions have been well explored1 and present opportunities to use AI for powerful assistance in thinking tasks. The core of the Industrial Revolution 4.0 is the adoption of AI methods. This revolution has affected all aspects of human activities and medicine is one example. AI systems can usually include formal algorithms for subtasks that can be solved using logic, for example, a decision tree. The task solution process moves from logic point to logic point similar to a train on a railway. These algorithms are fast and have the ability to explain. One of the most common is well described in the publication of Fei Jiang et al.2 in 2017, which divides AI features into language processing and machine learning tasks. In conjunction with his colleagues, he published different algorithms in the Pubmed database and found that the most frequently used are support vector machines, neural networks, logistic regression, discriminant analysis, random forest, linear regression, naïve bayes, nearest neighbor, decision tree, and hidden Markov. The goal of this study was to show qualitative change to AI development that has occurred over the last two years by examining the trends in Pubmed publications, including dynamics interest in AI topic, dynamics of non-English language publications, and implementations of AI in modern practice. The literature for this research included books related to the topic, included Goodfellow's Deep Learning3 and a Deep Learning in R.4 We also used Google patent search, specialized journal “Artificial intelligence” articles, and the Pubmed database. All abstracts with keyword “artificial intelligence” have been downloaded to txt files from the Pubmed database https://www.ncbi.nlm.nih.gov/pubmed/. Microsoft Access Visual Basic program was used to import these abstracts into the database. We then performed machine analysis of the dataset. The dataset includes names of all authors, all Mesh tags, year, language, and all words from the title and abstract. This information was extracted to tables, which were linked to the table of abstracts using a unique key. All non-informative words were marked as non-informative, and then the remaining keywords were grouped with generalizing words. We classified the present applications of AI in medicine by generalizing topics. We then made structured query language (SQL) queries to make frequency tables. Using this research, we classified the AI technologies. A total of 78,420 abstracts were extracted, including 30,835 journal articles, 37,332 research supports, 5558 reviews, 304 randomized controlled trials, 247 multicenter studies, and 4137 other publication types. We observed that the exponential growth of interest in AI solutions slowed down in middle of 2010. This followed the typical phenomenon of “S”-like development of innovation curves. It illustrates effectiveness of old technology and predicts the stagnation or a new stage of development.5 English was the most often used language in publication, followed by Chinese and then German. Non-English publications decreased over the last five years, especially Chinese publications. Fig. 1 shows that around 2010, interest to AI in oncology accelerated. The main reason was the development of medical visualization AI. It was mostly targeted to recognize tumors on images and the genome (Fig. 2). Interest in artificial intelligence in different medicine fields. Most frequent tasks performed with artificial intelligence. From this dataset, 809,451 Mesh tags wer