Artificial intelligence-guided analysis of cytologic data.

A design for the integration of artificial intelligence (AI) technology with large databases of clinical and objective cytologic data, such as are on file at the University of Chicago, is presented. Among the key features of this approach are the use of a knowledge representation structure based upon an associative network, the use of a Bayesian belief network as a method of managing uncertainty in the system, and the use of neural networks and unsupervised learning algorithms as a means of discovering patterns within this database. Such an automated approach is necessary, given the complexity and interdependence of these data, to gain an understanding of their dependence structure and to assist in their exploration and analysis.