Historical Analysis of Medical Artificial Intelligence Development in China: Research Centered on the expert system of traditional Chinese medicine

This study is based on a historical analysis of the technological development and introduction of artificial intelligence for medical use in China, based on the progress of application of artificial intelligence technology development to the medical field of traditional Chinese medicine and the experience of many experts in clinical practice. Through the application history of Traditional Chinese Medicine（TCM）expert systems technology, we will review its historical characteristics and the necessity of TCM expert system technology. By examining the application cases of TCM expert systems, we can find that Traditional Chinese Medicine（TCM）expert systems play an important role as an auxiliary means of diagnosis and treatment for doctors. This paper finds that the development of Traditional Chinese Medicine（TCM）expert systems is directly related to the change of Chinese government's science and technology policy through the investigation of the application history of Traditional Chinese Medicine（TCM）expert systems. At the same time, because the science and technology policy directly related to the traditional Chinese Medicine（TCM）expert systems appeared late, so the implementation technology of the traditional Chinese Medicine（TCM）expert systems is still insufficient. The technical level adopted in the implementation of the existing traditional Chinese Medicine（TCM）expert systems is still in a relatively low state. Therefore, whether in hardware or software, we should develop a new integrated traditional Chinese Medicine（TCM）expert systems based on the treatment theory of many Traditional Chinese Medicine（TCM）expert systems, so as to meet the needs of modern Traditional Chinese Medicine（TCM）expert systems diagnosis, people's need for healthy life, and medical care. Finally, when studying the historical issues of Traditional Chinese Medicine （TCM）expert systems, the Chinese government's science and technology policy changes are also an integral part

Експертні системи в практичній медицині

Одними з перших експертних систем є медичні. У статті міститься огляд в історичній ретроспективі цього класу експертних систем, і визначаються деякі перспективи їх застосування і подальшого розвитку в практичній медицині.One of the first expert systems was medical. In article the review in a historical retrospective show of this class of expert systems contains, and some prospects of their application and the subsequent development in applied medicine are defined

Research Review: Application of Expert Systems in the Sciences

Author Institution: Department of Electrical Engineering, The University of AkronStudies from the field of artificial intelligence have given birth to a relatively new but rapidly growing technology known as expert systems. An expert system is a computer program which captures the knowledge of a human expert on a given problem, and uses this knowledge to solve problems in a fashion similar to the expert. The system can assist the expert during problem-solving, or act in the place of the expert in those situations where the expertise is lacking. Expert systems have been developed in such diverse areas as science, engineering, business, and medicine. In these areas, they have increased the quality, efficiency, and competitive leverage of the organizations employing the technology. During the 1980s, scientists and engineers have used this technology to search for oil, diagnose medical problems, and explore space. This paper provides an overview of this technology, highlights the major characteristics of expert systems, and reviews several systems developed for application in the area of science

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability

ART Neural Networks: Distributed Coding and ARTMAP Applications

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, Gaussian ARTMAP, and distributed ARTMAP. ARTMAP has been used for a variety of applications, including computer-assisted medical diagnosis. Medical databases present many of the challenges found in general information management settings where speed, efficiency, ease of use, and accuracy are at a premium. A direct goal of improved computer-assisted medicine is to help deliver quality emergency care in situations that may be less than ideal. Working with these problems has stimulated a number of ART architecture developments, including ARTMAP-IC [1]. This paper describes a recent collaborative effort, using a new cardiac care database for system development, has brought together medical statisticians and clinicians at the New England Medical Center with researchers developing expert systems and neural networks, in order to create a hybrid method for medical diagnosis. The paper also considers new neural network architectures, including distributed ART {dART), a real-time model of parallel distributed pattern learning that permits fast as well as slow adaptation, without catastrophic forgetting. Local synaptic computations in the dART model quantitatively match the paradoxical phenomenon of Markram-Tsodyks [2] redistribution of synaptic efficacy, as a consequence of global system hypotheses.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

Experimental software modeling of knowledge acquisition processes for automated knowledge bases construction in dynamic integrated expert systems

This paper analyzes the results of the experimental research of automated knowledge base construction for dynamic intelligent systems, in particular dynamic integrated expert systems on the basis of the so-called original combined method of knowledge acquisition with temporal extensions. The focus of this work is on some aspects of the application and development of technologies of knowledge acquisition from various sources (experts, NL- texts, data bases) in order to create new applied intellectual technologies that can be used, for example, in the field of healthcare (personalized medicine, "smart" hospital, etc.)

Consistency checking of binary categorical relationships in a medical knowledge base

Abstract Moser, W. and K-P. Adlassnig, Consistency checking of binary categorical relationships in a medical knowledge base, Artificial Intelligence in Medicine 4 (1992) 389-407. Knowledge bases of medical expert systems have grown to such an extent that formal methods to verify their consistency seem highly desirable; otherwise, decision results of such expert systems are not reliable and contradictory entries in the knowledge base may cause erroneous conclusions. Tbis paper presents a new formalization of the finding/finding, finding/disease, and disease/disease relationships of the medical expert system CALXAG-1. This formalization also helps to clarify the differences between the application of propositional logic and of quantificational logic to capture the meaning of some fundamental categorical relationships in the area of medical diagnostics. Moreover, this formalization leads to very simple yet provably correct and complete algorithms to check the consistency of a medical knowledge base containing a set of these relationships