Use of artificial intelligence in analytical systems for the clinical laboratory.

OBJECTIVE: To consider the role of software in system operation, control and automation, and attempts to define intelligence. METHODS AND RESULTS: Artificial intelligence (Al) is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of Al in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. CONCLUSION: Al constitutes a collective form of intellectual property, and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Use of artificial intelligence in analytical systems for the clinical laboratory.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks.This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system.In the second part of the paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories.It is concluded that AI constitutes a collective form of intellectual propery, and that there is a need for better documentation, evaluation and regulation of the systems already being used in clinical laboratories.

International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Incremento de la bioseguridad de sistemas analíticos en el laboratorio clínico / Increment of the biosecurity of analytical system in the clinic laboratory

La bioseguridad es una parte importante del conocimiento práctivo de todos los profesionales de laboratorio clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. En este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento (AU)

Incremento de la bioseguridad de sistemas analíticos en el laboratorio clínico / Increment of the biosecurity of analytical system in the clinic laboratory

La bioseguridad es una parte importante del conocimiento práctivo de todos los profesionales de laboratorio clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. En este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento

International Federation of Clinical Chemistry (IFCC): systematic top-down approach to clinical chemistry.

This paper introduces a systematic approach to organizing the discipline of clinical chemistry. The approach is called a top-down, systems approach because it starts at the top with the most general concepts and works down through less general concepts to the most specific details and techniques. The hypothesis is that the discipline can be organized into hierarchical levels of functional processes and operational approaches to those processes. The functional processes represent what clinical scientists do; the operational approaches represent how they do it. Because functional processes change little, if at all, with time, they are use to develop a stable infrastructure or framework for the discipline. That infrastructure is then used to organize and understand operational approaches that tend to change rapidly with time in response to technological advances. This paper begins with the most general functional processes and then uses selected examples of the more general functions to illustrate lower hierarchical levels of functional processes and operational approaches.

Increasing the biosafety of analytical systems in the clinical laboratory.

Biosafety is an important part of the know-how of all clinical laboratory professionals. Biosafely must have high priority in the design and use of analytical systems. Attention should be focused on reducing the handling of biological specimens, reducing biohazards to laboratory personnel, and on improving the labelling and containment of biohazardous materials. In this paper, biosafety issues are discussed in relation to the design of analytical systems, their use and maintenance.

Incremento de la bioseguridad de sistemas análiticos en el laboratorío clínico

La bioseguridad es una parte importante del conocimiento práctico de todos los profesionales de laboratorios clínicos. Debe focalizarse la atención en la reducción del manipuleo de especímenes biológicos, en la reducción de los materiales biológicos peligrosos para el personal de laboratorio y en el mejoramiento del rotulado y envasado de los materiales biopeligrosos. en este artículo, los temas de bioseguridad se discuten en relación al diseño de sistemas analíticos, su utilización y su mantenimiento

Systematic top-down approach to clinical chemistry.

This paper introduces a systematic approach to organizing the discipline of clinical chemistry. The approach is called a top-down, systems approach because it starts at the top with the most general concepts and works down through less general concepts to the most specific details and techniques. The hypothesis is that the discipline can be organized into hierarchical levels of functional processes and operational approaches to those processes. The functional processes represent what clinical scientists do; the operatinal approaches represent how they do it. Because functional processes change little, if at all, with time, they are used to develop a stable infrastructure or framework for the discipline. That infrastructure is then used to organize and understand operational approaches that tend to change rapidly with time in response to technological advances. The paper begins with the most general functional processes and then uses selected examples of the more general functions to illustrate lower hierarchical levels or functional processes and operational approaches.