Literature DB >> 33058789

Artificial Intelligence in Screening Mammography: A Population Survey of Women's Preferences.

Yfke P Ongena1, Derya Yakar2, Marieke Haan3, Thomas C Kwee4.   

Abstract

OBJECTIVE: To investigate the general population's view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms.
METHODS: Dutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error.
RESULTS: Of the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with "neither agree nor disagree." The item AI as a second reader was mostly responded with "neither agree nor disagree" (37.1%) and "agree" (37.6%), whereas the two last items on developer's and radiologist' responsibilities were mostly answered with "neither agree nor disagree" (44.6% and 39.2%, respectively). DISCUSSION: Despite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.
Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; breast cancer; mammography; mass screening; surveys and questionnaires

Year:  2020        PMID: 33058789     DOI: 10.1016/j.jacr.2020.09.042

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  3 in total

1.  Impact of artificial intelligence in breast cancer screening with mammography.

Authors:  Lan-Anh Dang; Emmanuel Chazard; Edouard Poncelet; Teodora Serb; Aniela Rusu; Xavier Pauwels; Clémence Parsy; Thibault Poclet; Hugo Cauliez; Constance Engelaere; Guillaume Ramette; Charlotte Brienne; Sofiane Dujardin; Nicolas Laurent
Journal:  Breast Cancer       Date:  2022-06-28       Impact factor: 3.307

2.  Women's attitudes to the use of AI image readers: a case study from a national breast screening programme.

Authors:  Niamh Lennox-Chhugani; Yan Chen; Veronica Pearson; Bernadette Trzcinski; Jonathan James
Journal:  BMJ Health Care Inform       Date:  2021-03

3.  Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey.

Authors:  Thomas Ploug; Anna Sundby; Thomas B Moeslund; Søren Holm
Journal:  J Med Internet Res       Date:  2021-12-13       Impact factor: 5.428

  3 in total

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