TRUST-CARE: A RISK-ADAPTIVE AI GOVERNANCE FRAMEWORK FOR TRUSTWORTHY LARGE LANGUAGE MODEL DEPLOYMENT IN REGULATED HEALTHCARE INFRASTRUCTURE

Abstract

Large language models (LLMs) are rapidly transforming the landscape of healthcare, bringing new opportunities for clinical information management, decision support, documentation, and knowledge-intensive services. However, when these models are applied to regulated health care environments, the risks are not just about the accuracy of the models; they need to be addressed. Hallucinated content, privacy exposure, unreliable evidence, inappropriate automation, adversarial inputs, and uncertain model behaviour may compromise the accountability and safety of AI-assisted healthcare processes. Existing governance models are often based on pre-established controls and thereby have limited ability to rapidly adapt protection based on the varying risk levels of individual requests and clinical situations. Therefore, this study presents a TRUST-CARE, risk-adaptive AI governance framework that incorporates the governance process into the lifecycle of LLMs. The proposed system is based on a combination of contextual risk assessment, analysis of evidence reliability, processing of privacy-aware evidence, retrieval-based generation, hallucination verification, confidence calibration, and risk-triggered human oversight. The proposed methodology compares the performance of TRUST-CARE with the baseline configuration, conventional LLM, healthcare LLM, retrieval-augmented generation, and safety-guarded versions of the two baselines on the basis of task performance, evidence grounding, hallucination rate, calibration, privacy exposure, robustness, security, and expert trustworthiness assessment. The proposed architecture helps determine whether an AI response can be generated automatically, when there is a need for further evidence checking, and when human intervention must be mandated. The governance framework model provides a structured mechanism to link model behaviour with evidence quality, clinical risk, uncertainty and accountability. TRUST-CARE thus advances the standard safety controls in healthcare towards risk-sensitive, evidence-based, auditable AI governance under human supervision.