Stanford University
Department of Internal Medicine
School of Medicine
Palo Alto, California
Russell J. Cucina, MD

University of California, Davis
School of Medicine
Davis, California
Margrit M. Juretzka

Mastering basic analysis of gross pathological specimens is a fundamental task for students of medical pathology, a skill which informs the student’s clinical judgement by providing an organ system-level understanding of diseases as they present in patients. Mastery of gross analysis is complicated by the fact that students may have only recently learned the relevant anatomy, and may be only concurrently acquiring an understanding of the pathophysiological processes represented. Learning the range of normals and the varied presentations of abnormals requires exposure to a large number of high-quality specimens, which is difficult to achieve given the scarcity and expense of high-quality teaching cases and time constraints within expanding curricula.

WWW-ICIARC provides an expandable, remotely available library of gross specimen teaching cases for self-paced student practice, presented in an easily grasped analytical algorithm. Students are presented with a color digital photograph of a specimen, and work through a decision tree to draw conclusions about the pathological process represented. Each decision point is supported by an optional tutorial on the branches from that point, and students who err are directed to a relevant tutorial and may make additional attempts. At the end of each decision tree, students are offered examples of pathological entities which are consistent with the characteristics they have described, and are provided a reference to a widely used textbook of pathology for further reading. With affordable digital photography now widely available, the system prototype can be readily expanded, allowing clinical specimens to be routinely added to the teaching library.