Intelligent agent support for automated radiology exam

A difficult problem in automatic medical image understanding is that for every image type such as x-ray and every body organ such as heart, there exist specific solutions that do not allow for generalization. Just collecting all the specific solutions will not achieve the vision of a computerized physician. To address this problem, we propose an intelligent agent approach that is based on agent-oriented programming and the concept of active fusion. The advantage of agent-oriented programming is that it combines the benefits of object-oriented programming and expert system. For radiology image understanding, we present a multi-agent system that is composed of two major types of intelligent agents: radiologist agents and patient agents. A patient agent asks for multiple opinions from radiologist agents in interpreting a given set of images and then integrates the opinions. A radiologist agent decomposes the image recognition task into smaller problems that are solved collectively by multiple intelligent sub-agents. Finally, we present a preliminary implementation and running examples of the multi-agent system.