{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:40:54Z","timestamp":1780400454122,"version":"3.54.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) has been applied in a number of breast screening settings with favourable results. While there are a limited number of studies exploring patient attitudes on the use of AI in breast screening, none to date have examined patient perceptions on the use of AI in the symptomatic setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Following institutional approval, anonymous questionnaires were given to all patients attending the symptomatic breast clinic imaging department from 08\/07\/2024 to 04\/10\/2024. The questionnaire included questions on participant demographics and opinion questions on the use of AI in breast imaging. Multinomial logistic regression was performed to examine the associations between sociodemographic characteristics and patients' views about AI use in breast imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      One thousand five hundred thirty-four participants completed the questionnaire. Most participants were aged 40\u201359 years(35.8%). Almost one-quarter had either a personal (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009372) or family history of breast cancer (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009367). 61.4% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009943) had some\/strong interest in AI. 46.3% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009711) agreed the use of AI in healthcare was a good idea, and 43.9% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009673) were indifferent. 61% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009935) agreed to a radiologist and AI tool reading their mammogram, and 66.9% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091026) disagreed with AI being the sole reader of their mammogram. Even if AI was shown to be more accurate, 66.1% of patients still prefer a radiologist to review their mammogram, and even if AI was shown to be more efficient, 75.4% prefer a radiologist.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Participants generally held favourable views towards the use of AI in healthcare. They welcome the use of AI as an adjunct for radiologists, but disagree with AI being the only reader of their mammogram.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Previous studies have explored patient attitudes on the use of AI in breast screening, but none to date have assessed this in the symptomatic setting<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The majority of participants welcome the use of AI as an adjunct for radiologists but disagree with AI being the sole reader of their mammogram<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>This study highlights the importance of patient education to illustrate the benefits and limitations of AI in healthcare and how AI might work in the symptomatic breast setting<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-12288-4","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:15:36Z","timestamp":1769015736000},"page":"4467-4477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8762-6192","authenticated-orcid":false,"given":"Sneha","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rory","family":"Crean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"O\u2019Driscoll","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caitriona","family":"Cahir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayley","family":"Briody","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie","family":"Bambrick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neasa","family":"NiMhuircheartaigh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niamh","family":"Hambly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deirdre","family":"Duke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maeve","family":"Mullooly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuala A","family":"Healy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"12288_CR1","doi-asserted-by":"publisher","first-page":"e703","DOI":"10.1016\/S2589-7500(23)00153-X","volume":"5","author":"K Dembrower","year":"2023","unstructured":"Dembrower K, Crippa A, Col\u00f3n E, Eklund M, Strand F (2023) Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. 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