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Describes the application of a LAMSTAR (LArge Memory STorage And Retrieval) neural network to medical diagnosis and medical information retrieval problems. The network is based on M.L. Minsky's (1980) knowledge lines (k-lines) theory of memory storage and retrieval in the central nervous system. It employs arrays of self-organized map modules, such that the k-lines are implemented via link weights (address correlation) that are updated by learning. The network also employs features of forgetting and of interpolation and extrapolation, and is thus able to handle incomplete data sets. It can deal equally well with exact and fuzzy information, thus making it specifically applicable to medical diagnosis where the diagnosis is based on exact data, fuzzy patient interview information, patient histories, observed images and test records. Furthermore, the network can be operated in a closed loop with search engines to intelligently use data from the Internet in a higher learning hierarchy. All of the above features are shown to make the LAMSTAR network suitable for medical diagnosis problems that concern large data sets of many categories that are often incomplete and fuzzy. Applications of the network to three specific medical diagnosis problems are described: two from nephrology and one related to an emergency-room drug identification problem. It is shown that the LAMSTAR network is hundreds, and even thousands, times faster in its training than backpropagation-based networks when used for the same problem with exactly the same information.