Medview - a computerised teaching aid in oral medicine and oral pathology

The problems to solve Oral medicine and oral pathology are disciplines that comprise diagnosis and treatment of diseases in the oral cavity and adjacent tissues. Oral medicine focuses on clinical diagnosis and treatment of mucosal lesions, including oral cancer, while oral pathology contributes to the diagnosis of these disorders at a microscopic level. Available teaching aids do not cover the full spectrum of diseases related to oral medicine and oral pathology and the clinical training is insufficient. There is a lack of the coordination between the two disciplines and language barriers hamper the international exchange of teaching materials. These are serious problems when one considers that dentists regularly survey a majority of the population and, thus, are in an ideal position to decrease morbidity and mortality as a consequence of oral diseases. The leading educational idea The leading idea is that the information gathered in the clinical setting should be immediately available to provide students with new knowledge. After years of discussion with students the following ideas have emerged to form the present project. * A large collection of clinical cases, including images, will give the students sufficient exposure to the various clinical manifestations of individual oral diseases. * A shorter time lag between collection of clinical data (disease history, status, clinical images, histopathology images, laboratory findings, etc.) and the production of teaching material will ensure that students are exposed rapidly to the full spectrum of oral diseases. * Interactive studies will enable students to extract new knowledge about populations of patients as well as individual cases. * Integration of data from both the clinical and the histopathological examinations will satisfy the requirement to coordinate the oral medicine and oral pathology aspects of cases. * A translater of clinical data and terms will enable students with different native languages to obtain new knowledge independent of the data's geographical origin.