title: Bayesian networks: computer-assisted diagnosis support in radiology.
abstract: Medical knowledge is growing at an explosive rate. While the availability of pertinent data has the potential to make the task of diagnosis more accurate, it is also increasingly overwhelming for physicians to assimilate. Using artificial intelligence techniques, a computer can process large amounts of data to help physicians manage the growing body of medical knowledge and thereby make better decisions. Computer-assisted diagnosis support is of particular interest to the diagnostic imaging community because radiologists must integrate huge amounts of data in order to diagnose disease. Bayesian networks, among the most promising artificial intelligence techniques available, enable computers to store knowledge and estimate the probability of outcomes based on probability theory. The article describes what a Bayesian network is and how it works using a system in mammography for illustration. A comparison of Bayesian networks with other types of artificial intelligence methods, specifically neural networks and case-based reasoning, clarifies the unique features and the potential of these systems to aid radiologists in the decisions they make every day.
authors.affiliation: University of Wisconsin Medical School, Department of Radiology, E3/311 Clinical Science Center, 600 Highland Avenue, Madison, WI 53792-3252, USA. es.burnside@hosp.wisc.edu
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
mesh_terms: D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D011871:Radiology / Q000379:methods*