Preventing Hallucinations in Clinical NLP using Adaptive Risk-Constrained Knowledge-Weighted Teacher-Student Distiller

Clinical Natural Language Processing (Clinical NLP) and Medical Artificial Intelligence systems are also becoming more dependent on big clinical data and language models to get a diagnosis and clinical decision-making. Although such models may be able to reason, they are normally characterized by hallucinations, incorrect inference as well as poor evidence grounding, which makes them dangerous when used in healthcare environments. The current LVLM- and AIGC-based clinical systems produce unsupported results because noisy data, low-domain alignment, and the lack of risk-conscious learning strategies cause unsupported results. In order to overcome these weaknesses, this paper suggests a hybrid workflow in clinical AI hallucination prevention. The framework incorporates Adaptive Multi-Mode Data Validation(AMMDV) to provide consistency on the schema, normalization of the data and semantic congruity of the clinical inputs.Evidence-Weighted Adapter Fine-Tuning (EWAF) uses adapters based on LoRA to fine-tune on medical sources. A Medical Hallucination Risk Score (MHRS) is a mixture of supervised alignment, KL-divergence and uncertainty-sensitive loss to identify high risk-outputs. Lastly, Adaptive Risk-Constrained Knowledge-Weighted Teacher-Student Distillation(ARCKW-TSD) allows risk-free deployment of student models with costly knowledge.