Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation

A new approach for mitigating hallucinations in healthcare LLMs has been proposed, featuring a fact-checking module independent of the LLM and a domain-specific summarization model fine-tuned using Low-Rank Adaptation (LoRA) on the MIMIC-III dataset. The fact-checking module employs numerical tests and logical checks, achieving a precision of 0.8904, recall of 0.8234, and an F1-score of 0.8556, while the LLM summary attained a ROUGE-1 score of 0.5797 and a BERTScore of 0.9120. This development is significant for practitioners as it enhances the reliability of LLM outputs in critical healthcare applications, improving patient safety and decision-making processes.