Glostrup/Copehagen, Denmark. segmented flow system started with the auto-analysers of Skeggs [2], that were made commercially available at the end of the 1950s. This process has continued through many phases with the most recent advances being in the area of robotics [3]. Mechanization figure) and the open or stand-alone system (total figure). Metarules are part of the procedural knowledge of expert systems. They are used to evoke programs and to guide the inference process and thus define how to use the rules in the system. They are basic blocks of (usually) IF_THEN rules for exhaustive mapping.The inference engine may make use of one or a number of these problem-solving mechanisms. Problems with expert systemsSince the expert system is built using knowledge from the expert, it may perform more or less like an expert.However, as an expert system does not get tired, it will give more consistent results than its human counterparts. A good expert system may even give better results than human experts performing at their best.

Glostrup/Copehagen, Denmark. segmented flow system started with the auto-analysers of Skeggs [2], that were made commercially available at the end of the 1950s. This process has continued through many phases with the most recent advances being in the area of robotics [3]. Mechanization figure) and the open or stand-alone system (total figure). Metarules are part of the procedural knowledge of expert systems. They are used to evoke programs and to guide the inference process and thus define how to use the rules in the system. They are basic blocks of (usually) IF_THEN rules for exhaustive mapping.The inference engine may make use of one or a number of these problem-solving mechanisms. Problems with expert systemsSince the expert system is built using knowledge from the expert, it may perform more or less like an expert.However, as an expert system does not get tired, it will give more consistent results than its human counterparts. A good expert system may even give better results than human experts performing at their best.

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.

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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.