Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test Interpretation in Clinical Medicine
<|reference_start|>Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test Interpretation in Clinical Medicine: Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using Retrieval-Augmented Generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 68 lab tests-30 with conditional factors and 38 without. For tests with factors, normal ranges depend on patient-specific information. Our results show that GPT-4-turbo with RAG achieved a 0.95 F1 score for factor retrieval and 0.993 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 29.1% in factor retrieval and showed 60.9% and 52.9% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.<|reference_end|>
@article{wang2024lab-ai,
    title={Lab-AI -- Retrieval-Augmented Language Model for Personalized Lab Test
  Interpretation in Clinical Medicine},
    author={Xiaoyu Wang, Haoyong Ouyang, Balu Bhasuran, Xiao Luo, Karim Hanna, Mia
  Liza A. Lustria, Zhe He},
    journal={arXiv preprint arXiv:2409.18986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18986},
    primaryClass={cs.CL cs.AI cs.IR}
}