Matthias Dietzel, Alexandra Resch, Pascal A T Baltzer
{"title":"[乳房成像中的人工智能:希望与挑战]。","authors":"Matthias Dietzel, Alexandra Resch, Pascal A T Baltzer","doi":"10.1007/s00117-024-01409-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Clinical/methodical issue: </strong>Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.</p><p><strong>Standard radiological methods: </strong>In principle, AI applications are available for all imaging modalities, with a particular focus on mammography in breast diagnostics.</p><p><strong>Methodical innovations: </strong>AI promises to filter examinations into negative and clearly positive findings, and thereby reduces part of the radiological workload. Other applications are not yet as widely established.</p><p><strong>Performance: </strong>AI methods for mammography, and to a lesser extent tomosynthesis, have already reached the diagnostic quality of radiologists.</p><p><strong>Achievements: </strong>Except for second-opinion applications/triage in mammography, most methods are still under development.</p><p><strong>Practical recommendations: </strong>Currently, most AI applications must be critically evaluated by potential users regarding their maturity and practical benefits.</p>","PeriodicalId":74635,"journal":{"name":"Radiologie (Heidelberg, Germany)","volume":" ","pages":"187-193"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11845416/pdf/","citationCount":"0","resultStr":"{\"title\":\"[Artificial intelligence in breast imaging : Hopes and challenges].\",\"authors\":\"Matthias Dietzel, Alexandra Resch, Pascal A T Baltzer\",\"doi\":\"10.1007/s00117-024-01409-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Clinical/methodical issue: </strong>Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.</p><p><strong>Standard radiological methods: </strong>In principle, AI applications are available for all imaging modalities, with a particular focus on mammography in breast diagnostics.</p><p><strong>Methodical innovations: </strong>AI promises to filter examinations into negative and clearly positive findings, and thereby reduces part of the radiological workload. Other applications are not yet as widely established.</p><p><strong>Performance: </strong>AI methods for mammography, and to a lesser extent tomosynthesis, have already reached the diagnostic quality of radiologists.</p><p><strong>Achievements: </strong>Except for second-opinion applications/triage in mammography, most methods are still under development.</p><p><strong>Practical recommendations: </strong>Currently, most AI applications must be critically evaluated by potential users regarding their maturity and practical benefits.</p>\",\"PeriodicalId\":74635,\"journal\":{\"name\":\"Radiologie (Heidelberg, Germany)\",\"volume\":\" \",\"pages\":\"187-193\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11845416/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiologie (Heidelberg, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s00117-024-01409-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiologie (Heidelberg, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s00117-024-01409-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/6 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
[Artificial intelligence in breast imaging : Hopes and challenges].
Clinical/methodical issue: Artificial intelligence (AI) is being increasingly integrated into clinical practice. However, the specific benefits are still unclear to many users.
Standard radiological methods: In principle, AI applications are available for all imaging modalities, with a particular focus on mammography in breast diagnostics.
Methodical innovations: AI promises to filter examinations into negative and clearly positive findings, and thereby reduces part of the radiological workload. Other applications are not yet as widely established.
Performance: AI methods for mammography, and to a lesser extent tomosynthesis, have already reached the diagnostic quality of radiologists.
Achievements: Except for second-opinion applications/triage in mammography, most methods are still under development.
Practical recommendations: Currently, most AI applications must be critically evaluated by potential users regarding their maturity and practical benefits.