Place J F, Truchaud A, Ozawa K, Pardue H, Schnipelsky P
International Federation of Clinical Chemistry, DAKO, Copenhagen, Denmark.
Clin Biochem. 1995 Aug;28(4):373-89. doi: 10.1016/0009-9120(95)00002-q.
To consider the role of software in system operation, control and automation, and attempts to define intelligence.
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.
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.
探讨软件在系统操作、控制及自动化中的作用，并尝试对智能进行定义。
人工智能（Al）的特点在于其处理不完整和不精确信息以及积累知识的能力。专家系统基于标准计算技术构建，严重依赖对其进行编程以呈现现实世界的领域专家和知识工程师。神经网络旨在模拟人类大脑的模式识别和并行处理能力，通过训练而非编程来实现。未来可能在于神经网络的识别能力与专家系统的合理化能力相结合。在本文的第二部分，给出了Al在知识工程和医学诊断的独立系统以及与临床实验室相关的故障检测、图像分析、用户界面、自然语言处理、机器人技术和机器学习的嵌入式系统中的应用示例。
Al构成了一种知识产权的集合形式，并且需要对临床实验室中已广泛使用的系统进行更好的文档记录、评估和监管。
