Background: Triple-Negative Breast Cancer (TNBC) is a lethal subtype of breast cancer with limited treatment options. The purpose of this Network Meta-Analysis (NMA) is to compare the efficacy and safety of inhibitors of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) in treating TNBC. Methods: Our search strategy was used in six databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature database, Embase, Scopus, and Web of Science up to November 2nd, 2022, as well as a thorough search in the most used trial registries. We included phase II and III randomized controlled trials that looked at the efficacy of PD-1/PD-L1 inhibitors in the treatment of TNBC and reported either Overall Survival (OS), Progression-Free Survival (PFS), or pathological Complete Response (pCR). The risk of bias was assessed utilizing Cochrane's risk of bias 2 tool, and the statistical analysis was performed using a frequentist contrast-based method for NMA by employing standard pairwise meta-analysis applying random effects model. Results: 12 trials (5324 patients) were included in our NMA including seven phase III trials. Pembrolizumab in a neoadjuvant setting achieved a pooled OS of 0.82 (95% Confidence Interval (CI) 0.65 to 1.03), a PFS of 0.82 (95% CI 0.71 to 0.94) and a pCR 2.79 (95% CI 1.07 to 7.24) compared to Atezolizumab’s OS of 0.92 (95% CI 0.74 to 1.15), PFS of 0.82 (95% CI 0.69 to 0.97), and pCR of 1.94 (95% CI 0.86 to 4.37). Atezolizumab had less grade ≥ 3 adverse events (OR 1.48, 95% CI 0.90 to 2.42) than Pembrolizumab (OR 1.90, 95% CI 1.08 to 3.33) in the neoadjuvant setting. Conclusions: PD-1/PD-L1 inhibitors exhibited varying efficacy in terms of OS, PFS, and pCR. They were associated with an increase in immune-related adverse effects. When used early in the course of TNBC, PD-1/PD-L1 inhibitors exert their maximum benefit. Durvalumab as a maintenance treatment instead of chemotherapy has shown promising outcomes. Future studies should focus on PD-L1 expression status and TNBC subtypes, since these factors may contribute to the design of individualized TNBC therapy regimens. Systematic review registration PROSPERO Identifier: CRD42022380712.

Breast cancer (BC) remains a major global health burden, consistently standing as the foremost contributor to cancer-related illness and death among women across the world. This meta-analysis aimed to evaluate the diagnostic accuracy of AI-assisted ultrasound elastography (UE) for BC detection by considering various factors, such as AI models, segmentation, cross-validation, data augmentation, the evaluation phase, and the addition of conventional ultrasound. PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, Scopus, and Web of Science were searched from inception to 22 June 2025, for observational studies using any AI-aided UE modality in BC classification compared to histopathology. We extracted binary diagnostic accuracy data and employed the split component synthesis method for pooled outcomes. Out of 501 identified records, 39 studies (6191 samples) were included in the meta-analysis. The overall diagnostic performance showed 90.3% sensitivity (95% confidence interval [CI] 86.4–93.1%), 88.0% specificity (95% CI 83.6%–91.4), a positive likelihood ratio of 7.5 (95% CI 5.4–10.5), a negative likelihood ratio of 0.110 (95% CI 0.078–0.156), and a diagnostic odds ratio of 68.3 (95% CI 42.3–110.1). Heterogeneity was substantial (I² = 78.0%), and the funnel plot demonstrated mild positive asymmetry. Subgroup analyses indicated improved diagnostic performance in studies that employed automatic or no segmentation, cross-validation, data augmentation, retrospective designs, models evaluated during the training phase, classical machine learning approaches, and the combination of B-mode ultrasound with elastography. Despite the presence of heterogeneity and the possibility of overestimation, AI-aided UE demonstrated superior diagnostic performance compared to UE alone. Researchers should consider adopting automatic segmentation, cross-validation, augmentation, and combining UE with conventional ultrasound. Our meta-analysis also explored the potential integration of AI-aided UE into breast cancer screening practices.