The complex relationships between continuously monitored health signals and therapeutic
regimens can be modelled via machine learning. However, the clinical implementation of …

The use of machine learning tools in healthcare is rapidly expanding. However, the
processes that support these tools in deployment, ie, machine-learning operations (MLOps) …

This article delves into the critical integration of domain knowledge into AI/ML systems
across various industries, highlighting its importance in developing ethically responsible …

Objectives Artificial intelligence (AI) proceeds through an iterative and evaluative process of
development, use, and refinement which may be characterized as a lifecycle. Within this …

Objective To derive a comprehensive implementation framework for clinical AI models within
hospitals informed by existing AI frameworks and integrated with reporting standards for …

This study underscores the complexities of implementing AI in clinical care, advocating for a
holistic approach that combines technical and sociotechnical elements. It stresses aligning …

В условиях быстрого развития компьютерных технологий непрерывно расширяется
область применения систем искусственного интеллекта (ИИ). В течение последних …

Artificial intelligence (AI) technologies have historically fallen short of their translational aims
because the technological perspective has dominated how the systems fit and function in …

The integration of AI and ML into the SDLC represents a groundbreaking advancement in
software engineering. This paper explores the transformative effects of AI-driven automation …