USE OF ARTIFICIAL-INTELLIGENCE IN ANALYTICAL SYSTEMS FOR THE CLINICALLABORATORY

Objective: To consider the role of software in system operation, contr ol and automation, and attempts to define intelligence. Methods and Re sults: Artificial intelligence (AI) is characterized by its ability to deal with incomplete and imprecise information and to accumulate know ledge. Expert systems, building on standard computing techniques, depe nd heavily on the domain experts and knowledge engineers that have pro grammed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel processing capabiliti es of the human brain and are taught rather than programmed. The futur e may lie in a combination of the recognition ability of the neural ne twork 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 a nd in embedded systems for failure detection, image analysis, user int erfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. Conclusion: AI constitutes a coll ective form of intellectual property, and that there is a need for bet ter documentation, evaluation and regulation of the systems already be ing used widely in clinical laboratories.