A Human–AI Co-Validation Framework to Mitigate LLM Hallucinations in Clinical Decision Support

Large Language Models (LLMs) offer powerful generative capabilities in clinical contexts but are prone to producing hallucinated outputs—plausible yet factually incorrect information—that threaten patient safety and clinical trust. To address this, we propose a Human–AI Co-Validation framework, integrating expert oversight with AI-driven verification in a preemptive, multi-layered validation loop. Unlike post-hoc explainability methods, our approach positions AI as a co-reasoning partner, enabling iterative detection and mitigation of potential hallucinations before clinical deployment. We implemented a proof-of-concept prototype for summarizing medication lists from synthetic patient records, leveraging GPT-4 Turbo for initial drafts, a rule-based human consistency check to flag inconsistencies, and an AI verification module employing knowledge-grounded reasoning, probabilistic confidence scoring, and cross-referencing with authoritative drug interaction and clinical guideline databases (FDA, AHRQ). Evaluation across 50 synthetic cases with polypharmacy and comorbidities demonstrated a substantial reduction in hallucination rates compared to baseline LLM outputs, validating the framework’s operational feasibility and robustness. Our findings underscore the critical role of human-in-the-loop supervision, explainable AI principles, and structured verification in enhancing reliability, trustworthiness, and translational potential of clinical AI systems. This framework establishes a new standard for preemptive validation, supporting scalable, generalizable, and safe deployment of LLMs in high-stakes medical decision-making, and provides a blueprint for integration with real-world electronic health records and dynamic clinical knowledge graphs. LLM hallucination, Clinical AI, Human-AI validation, Medical informatics, Patient safety, Explainable AI