Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations
Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging f...
Gespeichert in:
| Veröffentlicht in: | British journal of cancer 2021-07, Vol.125 (1), p.15-22 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 22 |
|---|---|
| container_issue | 1 |
| container_start_page | 15 |
| container_title | British journal of cancer |
| container_volume | 125 |
| creator | Hickman, Sarah E. Baxter, Gabrielle C. Gilbert, Fiona J. |
| description | Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far. |
| doi_str_mv | 10.1038/s41416-021-01333-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8257639</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2548469321</sourcerecordid><originalsourceid>FETCH-LOGICAL-c540t-9b0143c487eb2fbc94d6c88a636d45a5802d1e6e25f76f2457b95adaa5e5d4bc3</originalsourceid><addsrcrecordid>eNp9kTtPHTEQha0oCC6EP0ARWUqTgiV-PyiQEMpLQqJJasvr9S5Ge-2L7QXl38dwCSEpqKzRfOd4Zg4ARxidYETVp8Iww6JDBHcIU0q7-zdghTklHVZEvgUrhJDskCZoD-yXctNKjZTcBXuUSkkw0yswng9pU0OKMI3Q5hrG4IKdYYjVz3OYfHS-FbDP3pYKw9pOIU6n0N_ZebEPwmPo63VwTeNSLDXbJi3QxgHOYR3qI1PegZ3RzsUfPr0H4OeXzz8uvnWXV1-_X5xfdo4zVDvdI8yoY0r6noy902wQTikrqBgYt1whMmAvPOGjFCNhXPaa28Fa7vnAekcPwNnWd7P0az84H9tAs9nkNnj-ZZIN5t9ODNdmSndGES4F1c3g45NBTreLL9WsQ3HtFDb6tBRDOBJEakZFQz_8h96kJce2XqOYYkJTghtFtpTLqZTsx-dhMDIPMZptjKbFaB5jNPdN9P7lGs-SP7k1gG6B0lpx8vnv36_Y_gYCaKt0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2548469321</pqid></control><display><type>article</type><title>Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations</title><source>MEDLINE</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>SpringerLink Journals - AutoHoldings</source><creator>Hickman, Sarah E. ; Baxter, Gabrielle C. ; Gilbert, Fiona J.</creator><creatorcontrib>Hickman, Sarah E. ; Baxter, Gabrielle C. ; Gilbert, Fiona J.</creatorcontrib><description>Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.</description><identifier>ISSN: 0007-0920</identifier><identifier>EISSN: 1532-1827</identifier><identifier>DOI: 10.1038/s41416-021-01333-w</identifier><identifier>PMID: 33772149</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/67/1347 ; 692/700/1421 ; 692/700/3935 ; 706/648/697/129 ; Algorithms ; Artificial Intelligence ; Biomedical and Life Sciences ; Biomedicine ; Breast ; Breast - diagnostic imaging ; Cancer Research ; Drug Resistance ; Epidemiology ; Ethics ; Female ; Humans ; Medical ethics ; Molecular Medicine ; Oncology ; Practice Guidelines as Topic ; Prospective Studies ; Radiographic Image Interpretation, Computer-Assisted - methods ; Retrospective Studies ; Review ; Review Article</subject><ispartof>British journal of cancer, 2021-07, Vol.125 (1), p.15-22</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-9b0143c487eb2fbc94d6c88a636d45a5802d1e6e25f76f2457b95adaa5e5d4bc3</citedby><cites>FETCH-LOGICAL-c540t-9b0143c487eb2fbc94d6c88a636d45a5802d1e6e25f76f2457b95adaa5e5d4bc3</cites><orcidid>0000-0002-0124-9962</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257639/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8257639/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27922,27923,41486,42555,51317,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33772149$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hickman, Sarah E.</creatorcontrib><creatorcontrib>Baxter, Gabrielle C.</creatorcontrib><creatorcontrib>Gilbert, Fiona J.</creatorcontrib><title>Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations</title><title>British journal of cancer</title><addtitle>Br J Cancer</addtitle><addtitle>Br J Cancer</addtitle><description>Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.</description><subject>631/67/1347</subject><subject>692/700/1421</subject><subject>692/700/3935</subject><subject>706/648/697/129</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Breast</subject><subject>Breast - diagnostic imaging</subject><subject>Cancer Research</subject><subject>Drug Resistance</subject><subject>Epidemiology</subject><subject>Ethics</subject><subject>Female</subject><subject>Humans</subject><subject>Medical ethics</subject><subject>Molecular Medicine</subject><subject>Oncology</subject><subject>Practice Guidelines as Topic</subject><subject>Prospective Studies</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Retrospective Studies</subject><subject>Review</subject><subject>Review Article</subject><issn>0007-0920</issn><issn>1532-1827</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kTtPHTEQha0oCC6EP0ARWUqTgiV-PyiQEMpLQqJJasvr9S5Ge-2L7QXl38dwCSEpqKzRfOd4Zg4ARxidYETVp8Iww6JDBHcIU0q7-zdghTklHVZEvgUrhJDskCZoD-yXctNKjZTcBXuUSkkw0yswng9pU0OKMI3Q5hrG4IKdYYjVz3OYfHS-FbDP3pYKw9pOIU6n0N_ZebEPwmPo63VwTeNSLDXbJi3QxgHOYR3qI1PegZ3RzsUfPr0H4OeXzz8uvnWXV1-_X5xfdo4zVDvdI8yoY0r6noy902wQTikrqBgYt1whMmAvPOGjFCNhXPaa28Fa7vnAekcPwNnWd7P0az84H9tAs9nkNnj-ZZIN5t9ODNdmSndGES4F1c3g45NBTreLL9WsQ3HtFDb6tBRDOBJEakZFQz_8h96kJce2XqOYYkJTghtFtpTLqZTsx-dhMDIPMZptjKbFaB5jNPdN9P7lGs-SP7k1gG6B0lpx8vnv36_Y_gYCaKt0</recordid><startdate>20210706</startdate><enddate>20210706</enddate><creator>Hickman, Sarah E.</creator><creator>Baxter, Gabrielle C.</creator><creator>Gilbert, Fiona J.</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7TO</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0124-9962</orcidid></search><sort><creationdate>20210706</creationdate><title>Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations</title><author>Hickman, Sarah E. ; Baxter, Gabrielle C. ; Gilbert, Fiona J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c540t-9b0143c487eb2fbc94d6c88a636d45a5802d1e6e25f76f2457b95adaa5e5d4bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>631/67/1347</topic><topic>692/700/1421</topic><topic>692/700/3935</topic><topic>706/648/697/129</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Breast</topic><topic>Breast - diagnostic imaging</topic><topic>Cancer Research</topic><topic>Drug Resistance</topic><topic>Epidemiology</topic><topic>Ethics</topic><topic>Female</topic><topic>Humans</topic><topic>Medical ethics</topic><topic>Molecular Medicine</topic><topic>Oncology</topic><topic>Practice Guidelines as Topic</topic><topic>Prospective Studies</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Retrospective Studies</topic><topic>Review</topic><topic>Review Article</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hickman, Sarah E.</creatorcontrib><creatorcontrib>Baxter, Gabrielle C.</creatorcontrib><creatorcontrib>Gilbert, Fiona J.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>British journal of cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hickman, Sarah E.</au><au>Baxter, Gabrielle C.</au><au>Gilbert, Fiona J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations</atitle><jtitle>British journal of cancer</jtitle><stitle>Br J Cancer</stitle><addtitle>Br J Cancer</addtitle><date>2021-07-06</date><risdate>2021</risdate><volume>125</volume><issue>1</issue><spage>15</spage><epage>22</epage><pages>15-22</pages><issn>0007-0920</issn><eissn>1532-1827</eissn><abstract>Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist’s performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33772149</pmid><doi>10.1038/s41416-021-01333-w</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-0124-9962</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0007-0920 |
| ispartof | British journal of cancer, 2021-07, Vol.125 (1), p.15-22 |
| issn | 0007-0920 1532-1827 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8257639 |
| source | MEDLINE; EZB-FREE-00999 freely available EZB journals; PubMed Central; SpringerLink Journals - AutoHoldings |
| subjects | 631/67/1347 692/700/1421 692/700/3935 706/648/697/129 Algorithms Artificial Intelligence Biomedical and Life Sciences Biomedicine Breast Breast - diagnostic imaging Cancer Research Drug Resistance Epidemiology Ethics Female Humans Medical ethics Molecular Medicine Oncology Practice Guidelines as Topic Prospective Studies Radiographic Image Interpretation, Computer-Assisted - methods Retrospective Studies Review Review Article |
| title | Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T17%3A07%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Adoption%20of%20artificial%20intelligence%20in%20breast%20imaging:%20evaluation,%20ethical%20constraints%20and%20limitations&rft.jtitle=British%20journal%20of%20cancer&rft.au=Hickman,%20Sarah%20E.&rft.date=2021-07-06&rft.volume=125&rft.issue=1&rft.spage=15&rft.epage=22&rft.pages=15-22&rft.issn=0007-0920&rft.eissn=1532-1827&rft_id=info:doi/10.1038/s41416-021-01333-w&rft_dat=%3Cproquest_pubme%3E2548469321%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2548469321&rft_id=info:pmid/33772149&rfr_iscdi=true |