Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) and Breast Implant-Associated Squamous Cell Carcinoma (BIA-SCC) are emerging neoplastic complications related to breast implants. While BIA-ALCL is often linked to macrotextured implants, current evidence does not suggest an implant-type association for BIA-SCC. Chronic inflammation and genetics have been hypothesized as key pathogenetic players, although for both conditions, the exact mechanisms and specific risks related to breast implants are yet to be established. While the genetic alterations in BIA-SCC are still unknown, JAK-STAT pathway activation has been outlined as a dominant signature of BIA-ALCL. Recent genetic investigation has uncovered various molecular players, including MEK-ERK, PI3K/AKT, CDK4-6, and PDL1. The clinical presentation of BIA-ALCL and BIA-SCC overlaps, including most commonly late seroma and breast swelling, warranting ultrasound and cytological examinations, which are the first recommended steps as part of the diagnostic work-up. While the role of mammography is still limited, MRI and CT-PET are recommended according to the clinical presentation and for disease staging. To date, the mainstay of treatment for BIA-ALCL and BIA-SCC is implant removal with en-bloc capsulectomy. Chemotherapy and radiation therapy have also been used for advanced-stage BIA-ALCL and BIA-SCC. In-depth characterization of the tumor genetics is key for the development of novel therapeutic strategies, especially for advanced stage BIA-ALCL and BIA-SCC, which show a more aggressive course and poor prognosis.

Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly transforming the study, manufacturing, and clinical translation of adipose-derived stem/stromal cells (ADSCs). ADSC-based therapies face persistent challenges related to donor variability, heterogeneous cell populations, limited standardization of culture protocols, and the need for robust quality control (QC) and potency assessment under Good Manufacturing Practice (GMP) conditions. This review discusses how AI-driven approaches can support the ADSC pipeline from donor and tissue pre-screening, through isolation and expansion, to differentiation and batch release decisions. We highlight major methodological advances in computer vision and label-free imaging for monitoring morphology, confluency, proliferation, senescence, and contamination, as well as AI-assisted optimization strategies for culture parameters and differentiation protocols. In addition, we examine the growing role of multi-omics integration (transcriptomics, proteomics, metabolomics, and secretomics) combined with ML to predict functional potency, stratify donors, and identify biomarkers associated with therapeutic efficacy. Finally, we address current limitations, including data scarcity, inter-laboratory variability, model interpretability, and regulatory requirements, and outline future perspectives such as closed-loop bioprocess control, foundation models, and federated learning frameworks. Overall, AI offers a powerful toolkit to improve the reproducibility, safety, and scalability of ADSC manufacturing and to accelerate the development of standardized, data-driven regenerative medicine products.