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A neural-symbolic AI agent system for biomedical concept mapping.

Gongbo Zhang¹, Yilu Fang¹, Fangyi Chen¹

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA.

NPJ Digital Medicine

|April 4, 2026

Summary

Medical Concept Mapping (MCM) uses language models to improve biomedical concept linking accuracy. This novel approach enhances mapping for rare and abbreviated terms, outperforming existing methods.

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Area of Science:

Biomedical Informatics
Natural Language Processing

Background:

Concept mapping links free text to standardized biomedical terms.
Current methods (rule-based, learning-based) face challenges with ambiguity, scalability, and long-tail concept accuracy.

Purpose of the Study:

Introduce Medical Concept Mapping (MCM), an agentic workflow using language models.
Improve accuracy, interpretability, and robustness of biomedical concept normalization, especially for underrepresented and abbreviated concepts.

Main Methods:

MCM employs language models to rephrase ambiguous mentions into explicit descriptions before concept linking.
Evaluated across MedMentions, ST21pv, and MCN benchmarks against state-of-the-art baselines.

Main Results:

MCM achieved superior Recall@1 scores: 63.3 (MedMentions), 60.0 (ST21pv), and 67.9 (MCN).
Significantly improved zero-shot performance on abbreviated mentions, exceeding baselines by up to 24.8 Recall@1 points.
Human evaluation showed 79.4% of LLM-generated expansions were reasonable/useful, with GPT-OSS achieving 85.1% approval.

Conclusions:

MCM offers a more accurate, interpretable, and robust solution for long-tail concept normalization in biomedical applications.
The agentic workflow effectively addresses limitations of previous concept mapping approaches.