{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T01:52:41Z","timestamp":1781488361498,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T00:00:00Z","timestamp":1781395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) is reshaping ultrasound diagnosis by converting operator-dependent grayscale, Doppler, elastography, contrast-enhanced, automated-volume, and video data into reproducible decision support. In breast ultrasound, the most mature evidence involves benign\u2013malignant lesion classification, BI-RADS risk stratification, reduction in unnecessary biopsy in selected low-risk lesions, assistance for less experienced readers, automated breast volume scanning, video-based assessment, axillary staging, and prediction of biologic markers such as molecular subtype, HER2 status, Ki-67 expression, lymphovascular invasion, and nodal metastasis. AI does not replace sonographers, radiologists, pathologists, or clinical judgment; rather, it can standardize feature extraction, prompt second-reader review, quantify uncertainty, and integrate imaging with clinical context. This review summarizes current clinical applications of AI in ultrasound diagnosis, which has a strong recent multicenter evidence base. It also discusses implementation requirements, including standardized acquisition, external validation, calibration, imaging\u2013pathology concordance, workflow integration, data security, and equity across scanners and patient populations.<\/jats:p>","DOI":"10.3390\/diagnostics16121839","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:44:58Z","timestamp":1781484298000},"page":"1839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Artificial Intelligence in Breast Ultrasound Diagnosis"],"prefix":"10.3390","volume":"16","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Freiburg University, 79085 Freiburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Pfob","sequence":"additional","affiliation":[{"name":"Department of Breast Unit, Heidelberg University Hospital, 69120 Heidelberg, Germany"},{"name":"Department of Breast Center, Hospital St Elisabeth, 69121 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Reisig","sequence":"additional","affiliation":[{"name":"Department of Breast Unit, Heidelberg University Hospital, 69120 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3899-3346","authenticated-orcid":false,"given":"Lie","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Breast Unit, Heidelberg University Hospital, 69120 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S405","DOI":"10.1016\/j.jacr.2025.02.023","article-title":"ACR Appropriateness Criteria Supplemental Breast Cancer Screening Based on Breast Density","volume":"22","author":"Paulis","year":"2025","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_2","unstructured":"(2026, April 18). ACR Breast Imaging Reporting & Data System (BI-RADS). Available online: https:\/\/www.acr.org\/Clinical-Resources\/Reporting-and-Data-Systems\/Bi-Rads."},{"key":"ref_3","unstructured":"American College of Radiology (2021). ACR Practice Parameter for the Performance of a Diagnostic Breast Ultrasound Examination, American College of Radiology."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S146","DOI":"10.1016\/j.jacr.2023.02.013","article-title":"ACR Appropriateness Criteria Palpable Breast Masses","volume":"20","author":"Klein","year":"2023","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1007\/s11547-023-01694-7","article-title":"A Sonogram Radiomics Model for Differentiating Granulomatous Lobular Mastitis from Invasive Breast Cancer: A Multicenter Study","volume":"128","author":"Ma","year":"2023","journal-title":"Radiol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13546","DOI":"10.1038\/s41598-018-31906-4","article-title":"Radiomics of US Texture Features in Differential Diagnosis between Triple-Negative Breast Cancer and Fibroadenoma","volume":"8","author":"Lee","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4046","DOI":"10.1007\/s00330-021-08452-1","article-title":"The Diagnostic Performance of Ultrasound Computer-Aided Diagnosis System for Distinguishing Breast Masses: A Prospective Multicenter Study","volume":"32","author":"Wei","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1016\/j.acra.2023.11.031","article-title":"Artificial Intelligence in BI-RADS Categorization of Breast Lesions on Ultrasound: Can We Omit Excessive Follow-Ups and Biopsies?","volume":"31","author":"Guldogan","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"450","DOI":"10.2214\/AJR.23.29328","article-title":"Deep Learning\u2013Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise","volume":"221","author":"He","year":"2023","journal-title":"Am. J. Roentgenol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.2214\/AJR.19.21872","article-title":"Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment","volume":"214","author":"Mango","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.ejrad.2025.112012","article-title":"Impact of an Artificial Intelligence Decision Support System among Radiologists with Different Levels of Experience in Breast Ultrasound: A Prospective Study in a Tertiary Center","volume":"185","author":"Irmici","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1093\/bjr\/tqae233","article-title":"Ultrasound S-Detect System Can Improve Diagnostic Performance of Less Experienced Radiologists in Differentiating Breast Masses: A Retrospective Dual-Centre Study","volume":"98","author":"Du","year":"2025","journal-title":"Br. J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e223351","DOI":"10.1148\/radiol.223351","article-title":"Toward AI-Supported US Triage of Women with Palpable Breast Lumps in a Low-Resource Setting","volume":"307","author":"Berg","year":"2023","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1016\/j.ultrasmedbio.2022.06.019","article-title":"Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography","volume":"48","author":"Zhao","year":"2022","journal-title":"Ultrasound Med. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1007\/s00330-021-08330-w","article-title":"Radiomics Model Based on Shear-Wave Elastography in the Assessment of Axillary Lymph Node Status in Early-Stage Breast Cancer","volume":"32","author":"Jiang","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1097\/JS9.0000000000002105","article-title":"Elastography-Based AI Model Can Predict Axillary Status after Neoadjuvant Chemotherapy in Breast Cancer with Nodal Involvement: A Prospective, Multicenter, Diagnostic Study","volume":"111","author":"Huang","year":"2025","journal-title":"Int. J. Surg."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s00330-023-09995-1","article-title":"Ultrasound Radiomics-Based Nomogram to Predict Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter, Retrospective Study","volume":"34","author":"Du","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1016\/j.acra.2024.04.010","article-title":"Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study","volume":"31","author":"Zhang","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"oyaf090","DOI":"10.1093\/oncolo\/oyaf090","article-title":"Ultrasound-Based Deep Learning Radiomics for Enhanced Axillary Lymph Node Metastasis Assessment: A Multicenter Study","volume":"30","author":"Zhang","year":"2025","journal-title":"Oncologist"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103684","DOI":"10.1016\/j.ebiom.2021.103684","article-title":"Decoding the Molecular Subtypes of Breast Cancer Seen on Multimodal Ultrasound Images Using an Assembled Convolutional Neural Network Model: A Prospective and Multicentre Study","volume":"74","author":"Zhou","year":"2021","journal-title":"eBioMedicine"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1007\/s00330-020-07544-8","article-title":"Deep Learning with Convolutional Neural Network in the Assessment of Breast Cancer Molecular Subtypes Based on US Images: A Multicenter Retrospective Study","volume":"31","author":"Jiang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104706","DOI":"10.1016\/j.ebiom.2023.104706","article-title":"Deep Learning Radiopathomics Based on Preoperative US Images and Biopsy Whole Slide Images Can Distinguish between Luminal and Non-Luminal Tumors in Early-Stage Breast Cancers","volume":"94","author":"Huang","year":"2023","journal-title":"eBioMedicine"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10279","DOI":"10.1038\/s41598-025-94741-4","article-title":"Multi-Center Study: Ultrasound-Based Deep Learning Features for Predicting Ki-67 Expression in Breast Cancer","volume":"15","author":"Cen","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1007\/s00330-021-08271-4","article-title":"Predicting the Molecular Subtype of Breast Cancer and Identifying Interpretable Imaging Features Using Machine Learning Algorithms","volume":"32","author":"Ma","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2436050","DOI":"10.1080\/08941939.2024.2436050","article-title":"Integration of Longitudinal and Transverse Radiomics from Ultrasound Images with Clinical Factors for HER-2 Status Prediction in Invasive Breast Cancer Patients","volume":"37","author":"Wu","year":"2024","journal-title":"J. Investig. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1016\/j.acra.2024.01.023","article-title":"The Emergence of the Potential Therapeutic Targets: Ultrasound-Based Radiomics in the Prediction of Human Epidermal Growth Factor Receptor 2-Low Breast Cancer","volume":"31","author":"Du","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_27","unstructured":"(2026, April 17). ACR PRACTICE PARAMETER FOR THE PERFORMANCE OF WHOLE-BREAST ULTRASOUND FOR SCREENING AND STAGING. Available online: https:\/\/gravitas.acr.org\/PPTS\/GetDocumentView?docId=61."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1177\/0284185119858051","article-title":"Validation of Radiologists\u2019 Findings by Computer-Aided Detection (CAD) Software in Breast Cancer Detection with Automated 3D Breast Ultrasound: A Concept Study in Implementation of Artificial Intelligence Software","volume":"61","author":"Tan","year":"2020","journal-title":"Acta Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3924","DOI":"10.7150\/ijms.118430","article-title":"Deep Learning Based on Automated Breast Volume Scanner Images for the Diagnosis of Breast Lesions: A Multicenter Diagnostic Study","volume":"22","author":"Liu","year":"2025","journal-title":"Int. J. Med. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s40644-025-00892-y","article-title":"Whole-Lesion-Aware Network Based on Freehand Ultrasound Video for Breast Cancer Assessment: A Prospective Multicenter Study","volume":"25","author":"Han","year":"2025","journal-title":"Cancer Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.ultrasmedbio.2024.01.012","article-title":"Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study","volume":"50","author":"Chen","year":"2024","journal-title":"Ultrasound Med. Biol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104117","DOI":"10.1016\/j.medengphy.2024.104117","article-title":"Benign and Malignant Classification of Breast Tumor Ultrasound Images Using Conventional Radiomics and Transfer Learning Features: A Multicenter Retrospective Study","volume":"125","author":"Tian","year":"2024","journal-title":"Med. Eng. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9511","DOI":"10.1007\/s00330-021-08009-2","article-title":"Clinical Value of Radiomics and Machine Learning in Breast Ultrasound: A Multicenter Study for Differential Diagnosis of Benign and Malignant Lesions","volume":"31","author":"Romeo","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102560","DOI":"10.1016\/j.ejmp.2023.102560","article-title":"A Novel Deep Learning Model for Breast Lesion Classification Using Ultrasound Images: A Multicenter Data Evaluation","volume":"107","author":"Sirjani","year":"2023","journal-title":"Phys. Medica"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s40644-025-00879-9","article-title":"Hierarchical Diagnosis of Breast Phyllodes Tumors Enabled by Deep Learning of Ultrasound Images: A Retrospective Multi-Center Study","volume":"25","author":"Yan","year":"2025","journal-title":"Cancer Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1533033818763461","DOI":"10.1177\/1533033818763461","article-title":"The Feasibility of Classifying Breast Masses Using a Computer-Assisted Diagnosis (CAD) System Based on Ultrasound Elastography and BI-RADS Lexicon","volume":"17","author":"Fleury","year":"2018","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejca.2022.09.018","article-title":"Intelligent Multi-Modal Shear Wave Elastography to Reduce Unnecessary Biopsies in Breast Cancer Diagnosis (INSPiRED 002): A Retrospective, International, Multicentre Analysis","volume":"177","author":"Pfob","year":"2022","journal-title":"Eur. J. Cancer"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3482","DOI":"10.1200\/JCO-24-02681","article-title":"Deep Learning Model for Breast Shear Wave Elastography to Improve Breast Cancer Diagnosis (INSPiRED 006): An International, Multicenter Analysis","volume":"43","author":"Cai","year":"2025","journal-title":"J. Clin. Oncol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, S.Y., Li, Y.M., Fang, Y.Q., Jin, Z.Y., Li, J.K., Zou, X.M., Huang, S.S., Niu, R.L., Fu, N.Q., and Shao, Y.H. (2025). Contrast-Enhanced Ultrasound Radiomics Model for Predicting Axillary Lymph Node Metastasis and Prognosis in Breast Cancer: A Multicenter Study. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14632-9"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e71155","DOI":"10.1002\/cam4.71155","article-title":"Super-Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ","volume":"14","author":"Yang","year":"2025","journal-title":"Cancer Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1007\/s00330-022-09263-8","article-title":"Ultrasound-Based Deep Learning in the Establishment of a Breast Lesion Risk Stratification System: A Multicenter Study","volume":"33","author":"Gu","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1148\/radiol.2019182394","article-title":"Digital Mammography versus Digital Mammography Plus Tomosynthesis in Breast Cancer Screening: The Oslo Tomosynthesis Screening Trial","volume":"291","author":"Skaane","year":"2019","journal-title":"Radiology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5902","DOI":"10.1007\/s00330-020-07659-y","article-title":"A Deep Learning Model Integrating Mammography and Clinical Factors Facilitates the Malignancy Prediction of BI-RADS 4 Microcalcifications in Breast Cancer Screening","volume":"31","author":"Liu","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112356","DOI":"10.1016\/j.ejrad.2025.112356","article-title":"Dynamic AI-Assisted Ipsilateral Tissue Matching for Digital Breast Tomosynthesis","volume":"192","author":"Morrell","year":"2025","journal-title":"Eur. J. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in Cancer Detection and False-Positive Recall in Mammography Using Artificial Intelligence: A Retrospective, Multireader Study","volume":"2","author":"Kim","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2532889","DOI":"10.2214\/AJR.25.32889","article-title":"Commercial Artificial Intelligence Versus Radiologists: NPV and Recall Rate in Large Population-Based Digital Mammography and Tomosynthesis Screening Mammography Cohorts","volume":"225","author":"Chen","year":"2025","journal-title":"Am. J. Roentgenol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1038\/s41591-023-02625-9","article-title":"Prospective Implementation of AI-Assisted Screen Reading to Improve Early Detection of Breast Cancer","volume":"29","author":"Ng","year":"2023","journal-title":"Nat. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1186\/s40644-023-00643-x","article-title":"Breast Cancer Detection Accuracy of AI in an Entire Screening Population: A Retrospective, Multicentre Study","volume":"23","author":"Elhakim","year":"2023","journal-title":"Cancer Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s13058-018-1084-x","article-title":"Prospective Validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian Women","volume":"20","author":"Nickson","year":"2018","journal-title":"Breast Cancer Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1097\/RLI.0000000000000656","article-title":"Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women with Extremely Dense Breasts to Reduce False-Positive Diagnoses","volume":"55","author":"Verburg","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, S., Deng, B., Chen, J., Li, J., Wang, X., Li, G., Long, S., Wan, J., and Zhang, Y. (2025). Preoperative DBT-Based Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Multi-Center Study. BMC Med. Imaging, 25.","DOI":"10.1186\/s12880-025-01711-3"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e250281","DOI":"10.1148\/ryai.250281","article-title":"Breast Cancers Detected and Missed by AI-CAD: Results from the AI-STREAM Trial","volume":"8","author":"Chang","year":"2026","journal-title":"Radiol. Artif. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1093\/jbi\/wbae058","article-title":"External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers","volume":"7","author":"Plimpton","year":"2025","journal-title":"J. Breast Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1093\/jbi\/wbac046","article-title":"Multicenter, Multivendor Validation of an FDA-Approved Algorithm for Mammography Triage","volume":"4","author":"Retson","year":"2022","journal-title":"J. Breast Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7281","DOI":"10.1038\/s41467-021-27577-x","article-title":"A Machine and Human Reader Study on AI Diagnosis Model Safety under Attacks of Adversarial Images","volume":"12","author":"Zhou","year":"2021","journal-title":"Nat. Commun."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1839\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T01:08:48Z","timestamp":1781485728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/12\/1839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,14]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["diagnostics16121839"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16121839","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,14]]}}}