HyperShell: an expert system shell in a hypermedia environment--application in medical audiology

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.

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.