Engaging with artificial intelligence in Mammography Screening : Swedish breast radiologists’ views on trust, information and expertise
Högberg, Charlotte LU
; Larsson, Stefan
LU
and Lång, Kristina
LU
(2024)
In Digital Health
10.
p.1-13
- Abstract
- Objectives
Lack of trust and transparency is stressed as a challenge for clinical implementation of Artificial Intelligence (AI). In breast cancer screening, AI-supported reading shows promising results but more research is needed on how medical experts, that are facing the integration of AI into their work, reason about trust and information needs. From a sociotechnical information practice perspective, we add to this knowledge by a Swedish case study. This study aims to: 1) clarify Swedish breast radiologists’ views on trust, information and expertise pertaining to AI in mammography screening, and 2) analytically address ideas about medical professionals’ critical engagement with AI and motivations for trust in... (More) - Objectives
Lack of trust and transparency is stressed as a challenge for clinical implementation of Artificial Intelligence (AI). In breast cancer screening, AI-supported reading shows promising results but more research is needed on how medical experts, that are facing the integration of AI into their work, reason about trust and information needs. From a sociotechnical information practice perspective, we add to this knowledge by a Swedish case study. This study aims to: 1) clarify Swedish breast radiologists’ views on trust, information and expertise pertaining to AI in mammography screening, and 2) analytically address ideas about medical professionals’ critical engagement with AI and motivations for trust in AI.
Method
An online survey was distributed to Swedish breast radiologists. Survey responses were analysed by descriptive statistical method, correlation analysis, and qualitative content analysis. The results were used as foundation for analysing trust and information as parts of critical engagements with AI.
Results
Of the Swedish breast radiologists (n=105), 47 answered the survey (response rate=44.8%). 53.2% (n=25) of the respondents would to a high/somewhat high degree trust AI assessments. To a great extent, additional information would support the respondents’ trust evaluations. What type of critical engagement medical professionals are expected to perform on AI as decision-support remains unclear.
Conclusions
There is a demand for enhanced information, explainability and transparency of AI-supported mammography. Further discussion and agreement are needed considering what the desired goals for trust in AI should be and how it relates to medical professionals’ critical evaluation of AI-made claims in medical decision-support. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c3112817-26a3-446c-a08e-74cafce0fd2d
- author
- Högberg, Charlotte
LU
; Larsson, Stefan
LU
and Lång, Kristina
LU
- organization
- publishing date
- 2024-10-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artifical Intelligence, Breast cancer, Mass screening, Radiologists, Trust, Transparency, Information, Literacy, Expertise, AI
- in
- Digital Health
- volume
- 10
- pages
- 13 pages
- publisher
- SAGE Publications
- external identifiers
-
- scopus:85206116544
- pmid:39381821
- ISSN
- 2055-2076
- DOI
- 10.1177/20552076241287958
- project
- AI in the Name of the Common Good - Relations of data, AI and humans in health and public sector
- AIR Lund - Artificially Intelligent use of Registers
- Mammography Screening with Artificial Intelligence
- language
- English
- LU publication?
- yes
- id
- c3112817-26a3-446c-a08e-74cafce0fd2d
- date added to LUP
- 2024-09-10 13:19:09
- date last changed
- 2025-04-19 16:50:50
@article{c3112817-26a3-446c-a08e-74cafce0fd2d,
abstract = {{Objectives<br/>Lack of trust and transparency is stressed as a challenge for clinical implementation of Artificial Intelligence (AI). In breast cancer screening, AI-supported reading shows promising results but more research is needed on how medical experts, that are facing the integration of AI into their work, reason about trust and information needs. From a sociotechnical information practice perspective, we add to this knowledge by a Swedish case study. This study aims to: 1) clarify Swedish breast radiologists’ views on trust, information and expertise pertaining to AI in mammography screening, and 2) analytically address ideas about medical professionals’ critical engagement with AI and motivations for trust in AI.<br/><br/>Method<br/>An online survey was distributed to Swedish breast radiologists. Survey responses were analysed by descriptive statistical method, correlation analysis, and qualitative content analysis. The results were used as foundation for analysing trust and information as parts of critical engagements with AI.<br/><br/>Results<br/>Of the Swedish breast radiologists (n=105), 47 answered the survey (response rate=44.8%). 53.2% (n=25) of the respondents would to a high/somewhat high degree trust AI assessments. To a great extent, additional information would support the respondents’ trust evaluations. What type of critical engagement medical professionals are expected to perform on AI as decision-support remains unclear.<br/><br/>Conclusions<br/>There is a demand for enhanced information, explainability and transparency of AI-supported mammography. Further discussion and agreement are needed considering what the desired goals for trust in AI should be and how it relates to medical professionals’ critical evaluation of AI-made claims in medical decision-support.}},
author = {{Högberg, Charlotte and Larsson, Stefan and Lång, Kristina}},
issn = {{2055-2076}},
keywords = {{Artifical Intelligence; Breast cancer; Mass screening; Radiologists; Trust; Transparency; Information; Literacy; Expertise; AI}},
language = {{eng}},
month = {{10}},
pages = {{1--13}},
publisher = {{SAGE Publications}},
series = {{Digital Health}},
title = {{Engaging with artificial intelligence in Mammography Screening : Swedish breast radiologists’ views on trust, information and expertise}},
url = {{https://lup.lub.lu.se/search/files/196750093/hogberg-et-al-2024-engaging-with-artificial-intelligence-in-mammography-screening-swedish-breast-radiologists-views-on.pdf}},
doi = {{10.1177/20552076241287958}},
volume = {{10}},
year = {{2024}},
}