Impact of Data Presentation on Physician Performance Utilizing Artificial Intelligence-Based Computer-Aided Diagnosis and Decision Support Systems

Abstract

Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish between benign and malignant lesions on US, leading to false-positive and false-negative results, which limit the positive predictive value of lesions sent for biopsy (PPV3) and specificity. A recent study demonstrated that incorporating an AI-based decision support (DS) system into US image analysis could help improve US diagnostic performance. While the DS system is promising, its efficacy in terms of its impact also needs to be measured when integrated into existing clinical workflows. The current study evaluates workflow schemas for DS integration and its impact on diagnostic accuracy. The impact on two different reading methodologies, sequential and independent, was assessed. This study demonstrates significant accuracy differences between the two workflow schemas as measured by area under the receiver operating curve (AUC), as well as inter-operator variability differences as measured by Kendall’s tau-b. This evaluation has practical implications on the utilization of such technologies in diagnostic environments as compared to previous studies.

Physician workforce in the United States of America: forecasting nationwide shortages

“…As technology evolves, its impact on the medical field increases. For example, artificial intelligence (AI) algorithms will shift the roles of physicians from a knowledgebased role into more of a skills-based role [42]. All of that information will be available in an AI-driven database that can not only bring up the information at a moment's notice, but also help to diagnoses.…”

Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems

“…Thirty-seven publications were eventually included in this review. 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 Figure 1 presents the PRISMA flowchart.…”

“…Three studies reported on more than 1 CDSS (or using the same CDSS in different modalities). 21 , 31 , 42 A total of 107 main results were reported with statistical significance, and 41 were reported without it. Most studies defined statistical significance at P < .05, with some applying correction for multiple comparisons.…”

Künstliche Intelligenz in der Mammadiagnostik

“…Diese Features wurden mittels supervidiertem Lernen erstellt. Im Gegensatz zu älteren rigiden CAD-Systemen aus der Mammographie wird diese Software durch fortwährendes Einspielen neuer Bilder mit assoziierter pathologischer Korrelation dynamisch korrigiert und weiterentwickelt, was jedoch neue Herausforderungen an die Qualitätskontrolle stellt [3].…”