Breast cancer remains a significant global health challenge, necessitating accurate and timely diagnostic methods. While conventional B-mode ultrasound is a fundamental tool for evaluating breast lesions, its limitations in definitively differentiating benign from malignant masses often lead to unnecessary biopsies. Sonographic elastography, an advanced ultrasound technique, assesses tissue stiffness, a key indicator of malignancy. This article provides a comprehensive review of the diagnostic efficacy of various sonographic elastography parameters, including qualitative scoring systems and quantitative strain ratios, in characterizing breast lesions. We explore how these parameters, particularly when combined with conventional B-mode and Doppler ultrasonography, enhance diagnostic accuracy, reduce the need for invasive procedures, and improve clinical decision-making. The discussion highlights the transformative role of elastography in modern breast imaging, contributing to more precise and patient-friendly diagnostic pathways.

This article presents a theoretical and analytical review of the applicability of the QAPI (Quality Assurance and Performance Improvement) methodology for risk management in home-based palliative care. The study is based on an interdisciplinary approach that integrates systems theory, the Donabedian model, quality of care assessment tools, and digital monitoring algorithms. Particular attention is given to aligning structure, process, and outcome indicators with empirical data on patient needs and organizational barriers specific to outpatient settings. Sources covering patient-centered care, resource constraints, multicultural contexts, and care digitalization are analyzed. Based on regression models and content analysis of the literature, key risks are identified, including emotional burnout, informational deficits, inadequate symptom control, and insufficient spiritual support. A conceptual model for QAPI integration is proposed, which incorporates both technical and humanitarian aspects of quality. The developed framework includes indicators adapted to the context of home care, digital visualization tools, and principles for sustainable implementation under resource constraints. This article will be of interest to researchers in palliative medicine, quality management professionals, outpatient care coordinators, and all those involved in developing and implementing patient-centered systems for evaluating and improving home-based care.

The development and commercialization of pharmaceutical and MedTech products represent one of the most complex and resource-intensive endeavors in the modern economy. The process involves long timelines, high attrition rates, and the integration of vast volumes of structured and unstructured data. Generative artificial intelligence (GenAI) has emerged as a transformative tool capable of enhancing efficiency, accelerating scientific discovery, and streamlining operations across the life sciences value chain - from early research to clinical development, manufacturing, medical affairs, and commercialization.

A synthesis of current literature, real-world implementations, and industry benchmarks was conducted to evaluate high-impact application areas of GenAI in life sciences. Documented use cases from biopharmaceutical and MedTech organizations illustrate the deployment of GenAI in target identification, de novo molecule generation, trial protocol design, medical writing automation, manufacturing deviation analysis, medical engagement support, and omnichannel content generation.

Estimates indicate that GenAI could unlock between $60 billion and $110 billion in annual value across the pharmaceutical industry. The greatest economic potential lies in commercial functions, followed by research and clinical development. Early adopters - including Pfizer, Novartis, AstraZeneca, and Novo Nordisk - have reported productivity improvements ranging from 20% to 60% in pilot programs focused on regulatory documentation, manufacturing quality, and HCP engagement.

Despite these benefits, large-scale adoption remains constrained by several challenges. Key barriers include hallucination risks in language models, regulatory ambiguity, limitations in technical infrastructure and data quality, and resistance to organizational change. Addressing these constraints will be critical to ensuring the safe, compliant, and impactful integration of GenAI technologies into life sciences workflows.