Purpose: Exosome-surface enhanced Raman spectroscopy-artificial intelligence platform (exosome-SERS-AI) is an innovative liquid biopsy method that acquires SERS signals from plasma exosomes and analyzes them using deep learning models to diagnose cancer. This study aimed to evaluate whether exosome-SERS-AI could increase the diagnostic accuracy of ultrasonography (US) for suspicious breast lesions. Methods This prospective multicenter study enrolled 500 patients between November 2024 and December 2025. Eligible participants will be women aged ≥ 40 years who will undergo US performed by specialized breast radiologists and have suspicious breast lesions assigned to a Breast Imaging Reporting and Data System (BI-RADS) category 3–5 assessment. A 6 mL whole blood sample was collected from each participant. After plasma separation, SERS, which is highly sensitive to exosomes, was employed to measure Raman signals, and the acquired data were processed using artificial intelligence algorithms. Following sampling, all patients underwent US-guided core needle biopsy for breast lesions classified as BI-RADS category 4 and 5, and 12-months of follow-up US for lesions classified as BIRADS category 3. Histopathological examination was used as the reference standard for BIRADS 4 and 5 lesions, whereas stability on 12-month follow-up US was used as the reference standard for BI-RADS 3 lesions. The cohort is expected to have an equal distribution of benign and malignant cases. The following outcome measures were compared between US alone and the combination of exosome-SERS-AI with US: sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve. Enrollment is expected to be completed by 2025, and the study results are expected to be presented in 2026.Discussion: This prospective multicenter study will evaluate the performance of exosome-SERSAI compared to US in women with BI-RADS categories 3–5. Participant enrollment is ongoing.