Shortliffe E H
Section on Medical Informatics, Stanford University School of Medicine, CA 94305.
Artif Intell Med. 1993 Apr;5(2):93-106. doi: 10.1016/0933-3657(93)90011-q.
Artificial intelligence in medicine (AIM) has reached a period of adolescence in which interactions with the outside world are not only natural but mandatory. Although the basic research topics in AIM may be those of artificial intelligence, the applied issues touch more generally on the broad field of medical informatics. To the extent that AIM research is driven by performance goals for biomedicine, AIM is simply one component within a wide range of research and development activities. Furthermore, an adequate appraisal of AIM research requires an understanding of the research motivations, the complexity of the problems, and a suitable definition of the criteria for judging the field's success. Effective fielding of AIM systems will be dependent on the development of integrated environments for communication and computing that allow merging of knowledge-based tools with other patient data-management and information-retrieval applications. The creation of this kind of infrastructure will require vision and resources from leaders who realize that the practice of medicine is inherently an information-management task and that biomedicine must make the same kind of coordinated commitment to computing technologies as have other segments of our society in which the importance of information management is well understood.
医学人工智能（AIM）已步入一个“青春期”阶段，在此期间，它与外界的交互不仅自然而且是必不可少的。尽管AIM的基础研究主题可能属于人工智能领域，但应用问题更广泛地涉及医学信息学这一广阔领域。就AIM研究由生物医学的性能目标驱动而言，AIM仅仅是广泛的研发活动中的一个组成部分。此外，对AIM研究进行充分评估需要了解研究动机、问题的复杂性以及对判断该领域成功标准的恰当定义。AIM系统的有效应用将依赖于通信和计算集成环境的开发，这种环境能够将基于知识的工具与其他患者数据管理及信息检索应用进行融合。创建此类基础设施将需要领导者具备远见卓识和资源，这些领导者要认识到医学实践本质上是一项信息管理任务，并且生物医学必须像我们社会中其他充分理解信息管理重要性的领域一样，对计算技术做出同样的协调一致的投入。
