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The escalating integration of large language models (LLMs) into clinical environments underscores the imperative for robust protocols to mitigate hallucination risks in safety-critical text generation. This conceptual manuscript introduces a novel benchmarking protocol designed to evaluate and govern hallucination sensitivity within clinical language models, emphasizing theoretical architectures that prioritize patient safety and decision integrity. Hallucination sensitivity, defined as the propensity of models to generate unsubstantiated or erroneous content in medical contexts, poses significant threats to diagnostic accuracy, treatment planning, and regulatory compliance. Drawing from interdisciplinary insights in artificial intelligence and healthcare informatics, we propose the hallucination sensitivity orchestration framework (HSOF). This multi-layered governance infrastructure incorporates dynamic sensitivity thresholds, contextual alignment mechanisms, and iterative feedback loops to orchestrate safe text outputs. This framework delineates core components, including sensitivity detection layers, clinical validation gateways, and adaptive mitigation strategies, all conceptualized without empirical testing to focus on architectural resilience. Key theoretical contributions include interpretive formulas for risk propagation and decision confidence, illustrating how hallucination vulnerabilities cascade through clinical workflows. By synthesizing recent literature on LLM hallucinations in biomedicine, this work advocates for proactive protocol designs that embed ethical safeguards and interoperability standards. Ultimately, HSOF serves as a blueprint for developers and clinicians to benchmark model behaviors theoretically, fostering trustworthy AI deployment in high-stakes healthcare systems. This approach not only addresses current gaps in safety-critical text generation but also anticipates future evolutions in clinical AI governance, promoting a paradigm shift toward hallucination-resilient intelligence infrastructures.

Large language models (LLMs) have rapidly advanced since the transformer architecture was introduced in 2017, with systems such as GPT-3, GPT-4, Med-PaLM, and Claude increasingly explored for applications in medical education, clinical documentation, decision support, and patient communication, raising both optimism and concerns regarding safety and reliability. This systematic review synthesizes evidence across studies retrieved from PubMed, arXiv, ACL Anthology, IEEE Xplore, and Google Scholar that empirically evaluated LLMs in clinical settings using quantitative performance metrics, with risk of bias assessed using an adapted PROBAST framework for machine learning research. Findings show that LLMs achieve 60–90% accuracy on USMLE-style examinations, with leading models such as GPT-4 and Med-PaLM 2 reaching or surpassing passing thresholds, while in clinical documentation tasks they can reduce physician workload by approximately 30–50% in generating outputs such as discharge summaries, though human review remains consistently required. Performance in clinical decision support is more variable and specialty-dependent, and hallucination rates ranging from 5–30% have been reported, alongside persistent issues of bias and overconfidence in incorrect outputs. Overall, while LLMs demonstrate strong capabilities in structured medical knowledge tasks and documentation support, current limitations including hallucinations, bias, and lack of prospective clinical validation prevent safe autonomous deployment, making clinician oversight and robust safety safeguards essential for any clinical use.