Human-in-the-loop (HITL) architectures represent a critical framework for ensuring the safe and effective deployment of Generative AI in clinical settings. This article examines the design, implementation, and evaluation of HITL systems that strategically integrate clinician oversight into GenAI-driven healthcare applications. Despite the rapid adoption of AI technologies in healthcare environments, many implementations lack structured validation mechanisms, creating potential patient safety concerns. The article explores the inherent limitations of GenAI models in clinical contexts and presents evidence supporting the necessity of human oversight. It details the core components of effective HITL architectures, including explainability mechanisms, confidence scoring, contextual awareness, and feedback integration. Implementation strategies are examined across various clinical domains, including radiology, oncology, and intensive care, with domain-specific considerations highlighted. The article concludes with a framework for measuring effectiveness and ensuring continuous improvement of these systems through multidimensional metrics that capture both technical performance and real-world impact.
Keywords: Clinical Validation, Decision Support Systems, Human-AI collaboration, explainable AI, healthcare safety
