Adaptive Hallucination Mitigation Frameworks for Clinical Decision Support Systems Using Reinforcement Learning

Abstract

Large language models (LLMs) deployed in clinical decision support systems (CDSS) exhibit a critical failure mode known as hallucination, wherein the model generates factually incorrect, clinically implausible, or entirely fabricated medical information with unwarranted confidence. Such failures carry profound patient safety implications, necessitating robust mitigation strategies that extend beyond static post-hoc filtering approaches.

This paper presents an Adaptive Hallucination Mitigation Framework (AHMF) that leverages reinforcement learning (RL) to dynamically suppress hallucinated outputs in real-time clinical inference pipelines. The proposed framework formulates hallucination mitigation as a Markov decision process, wherein an RL agent learns a policy $\pi_\theta(a \mid s)$ that maps contextual clinical states $s \in \mathcal{S}$ to corrective actions $a \in \mathcal{A}$, optimizing a composite reward signal $\mathcal{R} = \alpha \mathcal{R}_{\text{factual}} + \beta \mathcal{R}_{\text{safety}} - \gamma \mathcal{R}_{\text{uncertainty}}$ that jointly encodes clinical factuality, patient safety constraints, and epistemic uncertainty penalties.

Empirical evaluations conducted across three benchmark clinical NLP datasets demonstrate that AHMF reduces hallucination rates by $38.4\%$ relative to baseline LLM deployments and achieves a $27.1\%$ improvement in clinical factual consistency as measured by established biomedical entailment metrics. Furthermore, the framework exhibits robust generalization across heterogeneous clinical domains including radiology, pharmacology, and differential diagnosis generation.

These findings establish that reinforcement learning constitutes a principled and effective paradigm for adaptive hallucination control in safety-critical medical AI systems, offering a scalable complement to retrieval-augmented generation and uncertainty quantification methodologies. The framework's modular architecture enables integration with existing hospital information systems without requiring full model retraining.