title: Expert system-controlled image display.
abstract: Conventional computer-based medical expert systems deliver advice to physicians as written text. While such advice is useful, it has distinct limitations in a visually oriented discipline such as diagnostic radiology, in which decisions often depend on pattern recognition and appreciation of subtle morphologic features. The authors developed a prototype expert computer system, IMAGE/ICON, which displays groups of images sorted into a series of axes based on different ways in which they may be similar. They may share a common feature, group of features, causes, or clinical setting. IMAGE/ICON may display examples of morphologic variations of a dominant finding or a spectrum of abnormalities seen in an specific disease or group of diseases. The system also assembles a written analysis of key features of a case. Such a tool may be useful as a diagnostic aid or for continuing medical education. It is likely to have particular impact in the form of an intelligent radiologic workstation, as picture archiving and communication systems become available.
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: P R
authors.initials: PR
authors.lastname: Fisher
authors.forename: A I
authors.initials: AI
authors.lastname: Cohn
authors.forename: P L
authors.initials: PL
authors.lastname: Miller
authors.forename: P G
authors.initials: PG
authors.lastname: Mutalik
mesh_terms: D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000175:diagnosis / Q000000981:diagnostic imaging; D011859:Radiography