Agent-Based Medical Diagnosis Systems

Medical diagnostics elaboration many times is a distributed and cooperative work, which involves more medical human specialists and different medical systems. Recent results described in the literature prove that medical diagnosis problems can be solved efficiently by large-scale medical multiagent systems. Cooperative diagnosing of medical diagnosis problems by large-scale multiagent systems makes the diagnoses elaborations easier and may increase the accuracy of elaborated diagnostics. The purpose of the study described in this paper consists in the development of a novel large-scale hybrid medical diagnosis system called LMDS. The LMDS system is composed from physicians, medical expert system agents developed in our previous works and medical ICMA agents. Medical ICMA agents represent a novel class of agents with the ICMA architecture developed in our previous works, endowed with medical diagnosis capability. The main novelty of the LMDS system consists in the novel classes of agent members of the system and the manner in which the members of the system contribute to the problems solving. Each diagnostics can be elaborated cooperatively by more members of the system. The diagnosis system can solve difficult medical diagnosis problems whose solving must be discovered cooperatively by the members of the system. Many difficult medical problem solving requires medical knowledge that cannot be detained by a single physician or a medical computational system. Simulations prove the correctness in operation of the LMDS system.