Literature DB >> 25459198

Improving the radiologist-CAD interaction: designing for appropriate trust.

W Jorritsma1, F Cnossen2, P M A van Ooijen3.   

Abstract

Computer-aided diagnosis (CAD) has great potential to improve radiologists' diagnostic performance. However, the reported performance of the radiologist-CAD team is lower than what might be expected based on the performance of the radiologist and the CAD system in isolation. This indicates that the interaction between radiologists and the CAD system is not optimal. An important factor in the interaction between humans and automated aids (such as CAD) is trust. Suboptimal performance of the human-automation team is often caused by an inappropriate level of trust in the automation. In this review, we examine the role of trust in the radiologist-CAD interaction and suggest ways to improve the output of the CAD system so that it allows radiologists to calibrate their trust in the CAD system more effectively. Observer studies of the CAD systems show that radiologists often have an inappropriate level of trust in the CAD system. They sometimes under-trust CAD, thereby reducing its potential benefits, and sometimes over-trust it, leading to diagnostic errors they would not have made without CAD. Based on the literature on trust in human-automation interaction and the results of CAD observer studies, we have identified four ways to improve the output of CAD so that it allows radiologists to form a more appropriate level of trust in CAD. Designing CAD systems for appropriate trust is important and can improve the performance of the radiologist-CAD team. Future CAD research and development should acknowledge the importance of the radiologist-CAD interaction, and specifically the role of trust therein, in order to create the perfect artificial partner for the radiologist. This review focuses on the role of trust in the radiologist-CAD interaction. The aim of the review is to encourage CAD developers to design for appropriate trust and thereby improve the performance of the radiologist-CAD team.
Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2014        PMID: 25459198     DOI: 10.1016/j.crad.2014.09.017

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  12 in total

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4.  Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.

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5.  Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

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Journal:  Med Biol Eng Comput       Date:  2022-07-02       Impact factor: 3.079

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Review 7.  Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.

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Journal:  BMJ Open       Date:  2021-01-29       Impact factor: 2.692

8.  The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice.

Authors:  Brian R Jackson; Ye Ye; James M Crawford; Michael J Becich; Somak Roy; Jeffrey R Botkin; Monica E de Baca; Liron Pantanowitz
Journal:  Acad Pathol       Date:  2021-02-16

9.  Deep Learning-Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke.

Authors:  Yung-Ting Chen; Yao-Liang Chen; Yi-Yun Chen; Yu-Ting Huang; Ho-Fai Wong; Jiun-Lin Yan; Jiun-Jie Wang
Journal:  Diagnostics (Basel)       Date:  2022-03-25

10.  Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

Authors:  Sonia Gaur; Nathan Lay; Stephanie A Harmon; Sreya Doddakashi; Sherif Mehralivand; Burak Argun; Tristan Barrett; Sandra Bednarova; Rossanno Girometti; Ercan Karaarslan; Ali Riza Kural; Aytekin Oto; Andrei S Purysko; Tatjana Antic; Cristina Magi-Galluzzi; Yesim Saglican; Stefano Sioletic; Anne Y Warren; Leonardo Bittencourt; Jurgen J Fütterer; Rajan T Gupta; Ismail Kabakus; Yan Mee Law; Daniel J Margolis; Haytham Shebel; Antonio C Westphalen; Bradford J Wood; Peter A Pinto; Joanna H Shih; Peter L Choyke; Ronald M Summers; Baris Turkbey
Journal:  Oncotarget       Date:  2018-09-18
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