AARD: A Risk-Aware Agentic AI Framework for Sequential Clinical Decision Support Using Large Language Models

Abstract

: Modern healthcare systems require decision-support tools that can operate effectively in complex, uncertain, and data-rich environments. This paper proposes the Adaptive Agentic Risk-aware Decision (AARD) framework, a policy-based approach for modelling clinical decision-making as a sequential and adaptive process. The framework integrates structured clinical data, such as vital signs and laboratory measurements, with un-structured clinical text, where Large Language Models (LLMs) are used to extract contextual representations. AARD employs an agentic architecture in which decisions are generated through a learned policy and refined using a risk-aware mechanism. This allows the system to adapt actions over time based on evolving state representations while incorporating safety considerations into the decision process. A recursive learning mechanism enables continuous policy updates using observed state transitions, supporting consistent decision behaviour across sequential steps. The framework is evaluated using open-source healthcare datasets, with performance assessed through both predictive metrics and decision-oriented measures. The results indicate that integrating multimodal representations with risk-aware policy learning provides a structured approach to sequential decision support. Overall, the proposed framework offers a scalable and interpretable approach for combining agentic decision processes, LLM-based feature extraction, and risk-aware optimisation in health-care settings.