title: Optimizing radiologic workup: an artificial intelligence approach.
abstract: The increasing complexity of diagnostic imaging is presenting an ever expanding variety of radiologic test options to clinicians. As a result, it is becoming more difficult for referring physicians to select an appropriate sequence of tests. The current economic pressures on medicine make it particularly important that resources be used judiciously. Radiologic workup often involves a sequence of tests that lead from presenting signs and symptoms to a definitive diagnosis or intervention. This sequence ideally begins with simple, inexpensive, safe, non-invasive tests and progresses to more complex, expensive, and hazardous tests only if the simpler tests are insufficient to establish a diagnosis. DxCON is a developmental artificial intelligence-based computer system that gives advice to physicians about the optimum sequencing of radiologic tests. DxCON evaluates basic clinical information and a physician's proposed workup plan. The system then creates an analysis of the strengths and weaknesses of his plan. The domain chosen to explore computer-based workup advice is the radiologic workup of obstructive jaundice.
authors.affiliation: Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT 06510.
authors.forename: H A
authors.initials: HA
authors.lastname: Swett
authors.forename: M
authors.initials: M
authors.lastname: Rothschild
authors.forename: G G
authors.initials: GG
authors.lastname: Weltin
authors.forename: P R
authors.initials: PR
authors.lastname: Fisher
authors.forename: P L
authors.initials: PL
authors.lastname: Miller
mesh_terms: D002779:Cholestasis / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging; D005103:Expert Systems; D006801:Humans