Background: Taxanes-induced neuropathy is common in BC patients receiving taxanes, forcing dose reductions and treatment delays and posing serious challenges for the long-term patient QoL. Discovering neuropathy predictors in patients could guide better treatment decisions, improved QoL and reduce healthcare costs. Belong digital PPN is a social network for cancer patients and caregivers that supports disease management. In this study we used our artificial intelligence (AI) engine to classify the prevalence, characteristics and taxanes-induced neuropathy status of BC patients. Methods: We analyzed real-world patient-reported outcomes provided voluntarily and anonymously from users on the Belong PPN. Data from BC patients reporting treatment with taxanes was extracted and additional analysis segmented the data to those who experienced neuropathy and those who did not. Further validation of the data was performed by our research team to assure accuracy. Results: We evaluated 169 BC cancer patients from the US treated with taxanes. In the cohort 72% were Paclitaxel-treated and 28% Docetaxel-treated at various disease stages: 68% at early stage BC (0-2) and 32% at the advanced/metastatic stages (3-4). 83% of Paclitaxel-treated patients and 67% of Docetaxel-treated patients reported experiencing neuropathy in the Belong platform. These real-world reports indicated significantly higher incidence of taxane-induced neuropathy in comparison to literature summarizing data from clinical trials, suggesting neuropathy incidence of 27% for paclitaxel and 16% for docetaxel (grades 2-4). Conclusions: Real-world patient-reported outcomes from the Belong PPN captured the prevalence of taxanes-induced neuropathy in BC patients and correlated it to the specific drug in use. Evidence for higher incidence of taxanes-induced neuropathy may lead to lower patient QoL and higher healthcare costs and should stimulate better treatment decisions. Further exploration of the gap between controlled clinical studies and real-world evidence is urgently needed to understand the true patient outcomes and optimize healthcare accordingly.