The fast development of technologies shapes the way companies address and understand their customers’ needs, including the more and more pressing call for sustainability. If, by now, many organizations use the advantages of social media in their marketing strategies, newer technologies, such as Internet of things (IoT) wearables, are not fully used to their whole potential. Thus, we conducted two research studies—a qualitative one in the form of a focus group where eight different companies’ representatives took part, followed by a quantitative one in the form of an online questionnaire, where 84 (Small and Medium Sized Enterprises) SMEs and Large Enterprises answered. The main purpose of our research was to investigate companies’ attitudes and practices about using social media and IoT wearable technologies in developing organizational marketing strategies. The results indicate that, though there are some differences in the perception and use of social media and IoT wearables for developing marketing strategies, these differences are not marked between SMEs and Large Enterprises, but rather between micro enterprises and other companies with higher numbers of employees. Additionally, there are some differences noticed between companies operating in regional, national, or international markets.

Accurate distinction between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) remains the key local staging problem in bladder cancer because treatment intensity, timing of radical therapy, and suitability for bladder-preserving strategies all depend on it. Multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) now provide a standardized imaging framework for local staging and increasingly support MRI-first clinical pathways. Artificial intelligence (AI) has emerged as an additional decision-support layer, but the evidence base remains methodologically uneven. In this structured narrative review, we synthesized peer-reviewed literature from January 2020 to March 2026, while retaining foundational VI-RADS studies from 2018 to 2019, and prioritized guideline documents, meta-analyses, prospective cohorts, multicenter and externally validated AI studies, response-assessment studies, and papers addressing implementation and reporting quality. Current evidence shows that radiomics and deep learning models can achieve high discrimination for MIBC detection on MRI, and that the most plausible incremental value of AI lies in equivocal VI-RADS lesions, reader support outside high-volume expert settings, and multimodal risk stratification. However, most studies remain retrospective, highly selected, segmentation-dependent, and vulnerable to reference-standard bias, domain shift, and poor calibration. This review therefore emphasizes several translational issues that are often underreported: lesion-level versus patient-level inference, the distortive effect of TURBT-based labels, the need to evaluate false-negative consequences in VI-RADS 3 tumors, and the distinction between diagnostic support and broader pathway redesign. We also discuss response assessment, nacVI-RADS, segmentation automation, multicenter and federated infrastructure, workflow ownership, and the limits of imaging-only models in a biologically heterogeneous disease. The most credible near-term role of AI is not autonomous diagnosis, but augmentation of standardized mpMRI and VI-RADS within multidisciplinary care. Future progress will depend on prospective utility studies, site-held-out validation, transparent reporting, and the integration of imaging with molecular and cellular heterogeneity through radiogenomic and multi-omics approaches.