In this article, we describe a framework for distributed statistical analysis of medical images that operates under minimal bandwidth requirements. This framework implements a distributed dynamic recursive partitioning algorithm for medical image analysis that integrates medical image data repositories that contain multiple studies and are potentially located at spatially distributed clinical sites. The goal is to detect associations among regions of interest (ROIs) in images and clinical properties such as the progression of a disease. Statistical descriptors are computed from the ROIs in order to assist in medical decision making by facilitating automatic characterization, classification, and similarity searches of ROIs (e.g., lesions, tumors, and regions of morphological variability). The system consists of a central information fusion site that coordinates the analysis by communicating with remotely located processing sites. The described system has the advantage of keeping bandwidth requirements to a minimum by reducing the amount of data that need to be transferred, while medical decision making is not affected by the data reduction. This benefit makes the proposed distributed medical image analysis framework very suitable for deploying effective telemedical applications.