A Systematic Review of Breast Cancer Imaging Using AI-Assisted Breast Ultrasound and Point-Of-Care Ultrasound (POCUS)

Background: Breast cancer remains the most diagnosed cancer among women worldwide. Early detection is critical for improving survival, yet access to high-quality imaging remains uneven, particularly in low-resource and rural settings. Ultrasound is widely used as an adjunct diagnostic modality and is increasingly deployed in portable and point-of-care ultrasound (POCUS) formats. From 2020–2025, artificial intelligence (AI), machine learning (ML), and deep learning (DL) methods have been integrated into breast ultrasound systems as software-based medical devices, enabling automated lesion assessment, risk stratification, and workflow support.
Objective: To systematically review peer-reviewed literature published between 2020 and 2025 on AI-assisted breast ultrasound technologies, with emphasis on early detection tools, medical device software, POCUS-based systems, and precision medicine approaches including radiomics and radiogenomics.
Methods: A PRISMA-aligned systematic review was conducted using PubMed. Eligible studies included peer-reviewed clinical trials, diagnostic accuracy studies, and systematic reviews evaluating AI-assisted breast ultrasound or POCUS systems for cancer detection or classification. Extracted outcomes included study design, device type, dataset size, reference standards, and diagnostic performance metrics.
Results: Included studies demonstrate that AI-assisted breast ultrasound systems, including regulated software-as-a medical-device (SaMD) platforms and AI-enabled POCUS workflows, achieve diagnostic performance comparable to or exceeding conventional radiologist assessment in selected contexts. That said, the existing studies are limited in number. Radiomics-based feature extraction and emerging radiogenomic approaches further support precision medicine objectives by linking imaging phenotypes with tumor biology. However, heterogeneity in datasets, imaging protocols, and validation methods limits cross-study comparability.
Conclusion: Between 2020 and 2025, AI-assisted breast ultrasound evolved from experimental CAD tools into clinically evaluated medical device software, including applications in POCUS and low-resource environments. The strongest evidence supports AI as a decision-support and triage tool rather than a standalone diagnostic replacement. Future research should prioritize prospective, multi center POCUS trials and standardized radiomics-omics integration to enable robust precision breast imaging.

Breast cancer; precision medicine; artificial intelligence; machine learning; deep learning; medical device software; ultrasound; point-of-care ultrasound; radiomics; radiogenomics

1. Xiao Y, Wu J, Lin Z, Zhao X. A deep learning–based multi-model ensemble method for cancer prediction using breast ultrasound images. Ultrasound Med Biol. 2020;46(11):2730-2740. doi: 10.1016/j.ultrasmedbio.2020.07.003 [Google Scholar] [Crossref]

2. Zhao C, Xiao M, Ding J, et al. A deep learning–based assistant system for breast ultrasound diagnosis: a prospective, multi center validation study. Eur Radiol. 2022;32(3):1777-1787. doi:10.1007/s00330021-08258-3 [Google Scholar] [Crossref]

3. Xiang W, Sun X, Wang L, et al. Deep learning–based computer-aided diagnosis of breast ultrasound images: multi center validation and reader performance study. Radiology. 2023;307(2):e222215. doi:10.1148/radiol.222215 [Google Scholar] [Crossref]

4. Dan J, Zhang H, Li Y, et al. Diagnostic performance of deep learning–based computer-aided diagnosis systems for breast ultrasound: a systematic review and meta-analysis. Eur J Radiol. 2024; 171:111347. doi: 10.1016/j.ejrad.2023.111347 [Google Scholar] [Crossref]

5. U.S. Food and Drug Administration. 510(k) Premarket Notification: Koios DS™ Breast. FDA; 2021. Accessed Month Day, Year. https://www.accessdata.fda.gov [Google Scholar] [Crossref]

6. Malherbe F, Adejolu M, Patel N, et al. Artificial intelligence–assisted point-of-care ultrasound for breast cancer risk stratification in low-resource settings: a prospective cohort study. J Ultrasound Med. 2025;44(1):87-98. doi:10.1002/jum.16521 [Google Scholar] [Crossref]

7. Oteibi M, Khazaei H, Abbas K, Balaguru B, Williams AR, Etesami F. Breast Imaging and Omics for Non-Invasive Integrated Classification (BIONIC). Int J Res Innov Appl Sci. 2025;10(8):94-110. doi:10.51584/IJRIAS.2025.100800094 [Google Scholar] [Crossref]

8. Oteibi M, Tamimi A, Abbas K, Tamimi G, Khazaei D, Khazaei H. Advancing digital health using artificial intelligence and machine learning solutions for early ultrasonic detection of breast disorders in women. Int J Res Sci Innov. 2024;11(11):39-55. doi:10.51244/IJRSI.2024.11110039 [Google Scholar] [Crossref]

9. Oteibi M, Tamimi A, Abbas K, Tamimi G, Khazaei D, Khazaei H. Breast tumor ultrasound: clinical applications, diagnostic features, and integration with artificial intelligence. Int J Res Innov Appl Sci. 2025;10(8):34-49. doi:10.51584/IJRIAS.2025.100800034 [Google Scholar] [Crossref]

10. Oteibi M, Tamimi A, Abbas K, Tamimi G, Khazaei D, Khajehee B, Khazaei H. AI-assisted point-ofcare ultrasound (POCUS) versus mammography for early breast cancer detection: a comparative review. Int J Res Innov Appl Sci. 2025;10(10):125-141. doi:10.51584/IJRIAS.2025.10100000125 [Google Scholar] [Crossref]

11. Oteibi M, Tamimi A, Abbas K, Tamimi G, Khazaei H, Khajehee B, Etesami F. AI-integrated breast point-of-care ultrasound (POCUS): image acquisition standard operating procedure (SOP). Int J Res [Google Scholar] [Crossref]

12. Innov Appl Sci. In press. published December 23, 2025. doi: 10.51584/IJRIAS.UMI.10IJ09ASN10243 [Google Scholar] [Crossref]

13. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563-577. doi:10.1148/radiol.2015151169 [Google Scholar] [Crossref]

14. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-446. doi: 10.1016/j.ejca.2011.11.036 [Google Scholar] [Crossref]

15. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in breast cancer. Eur Radiol. 2020;30(8):4099-4109. doi:10.1007/s00330-020-06741-9 [Google Scholar] [Crossref]

16. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697 [Google Scholar] [Crossref]

17. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71 [Google Scholar] [Crossref]

• Measuring Waste of Patient Time in Health Care at Non-Digitized Hospital: An Observational Study in Bangabandhu Sheikh Mujib Medical University, Bangladesh
• Reaffirming Clinical Confidence in Atorvastatin Therapy: A Digital Outreach Case Study from Tamil Nadu, India
• Clinical Manifestations and Therapeutic Response in a Patient with Hypothyroidism: A Case Report
• Eranda (Ricinus Communis) In Gridhrasi (Sciatica): Classical Rationale, Pharmacology and Clinical Evidence- A Narrative Literature Review
• Magnetotherapy in Pain Management: Mechanisms, Clinical Applications, and Future Perspectives – A Review