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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical

Xiaoyu Wang¹, Haoyong Ouyang¹, Balu Bhasuran²

¹Department of Statistics, Florida State University, Tallahassee, FL, USA.

Proceedings. IEEE International Conference on Healthcare Informatics

|December 18, 2025

Summary

This summary is machine-generated.

Lab-AI provides personalized lab result ranges using AI and health data. This system improves patient understanding by considering factors like age and gender, unlike standard universal ranges.

Keywords:

Lab test Interpretation Personalized Information Retrieval Retrieval Augmented Generation

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Pathology

Background:

Patient portals often use universal normal ranges for lab results.
This overlooks critical conditional factors such as age and gender.
Accurate interpretation of lab results is vital in clinical medicine.

Purpose of the Study:

To introduce Lab-AI, an interactive system for personalized lab result interpretation.
To leverage retrieval-augmented generation (RAG) for accessing credible health information.
To provide personalized normal ranges based on patient-specific data.

Main Methods:

Developed Lab-AI with two modules: factor retrieval and normal range retrieval.
Utilized GPT-4-turbo with RAG for system implementation.
Evaluated the system on 122 lab tests, including 40 with conditional factors.

Main Results:

GPT-4-turbo with RAG achieved a 0.948 F1 score for factor retrieval.
Achieved 0.995 accuracy for normal range retrieval.
Outperformed non-RAG systems significantly in both factor and normal range retrieval.

Conclusions:

Lab-AI demonstrates significant potential for enhancing patient comprehension of lab results.
Personalized normal ranges improve the accuracy of lab result interpretation.
RAG integrated with AI offers a powerful approach for clinical decision support.