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Home
Reframing Ai For Rare Disease Recognition.

Home
Reframing Ai For Rare Disease Recognition.

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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Published on: October 27, 2023

Reframing AI for Rare Disease Recognition.

Wei-Qi Wei¹, Chao Yan¹, Wu-Chen Su²

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Research Square

|April 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

GEN-KnowRD accelerates rare disease recognition by using large language models to build knowledge bases, improving diagnostic accuracy and reducing delays for patients. This framework enhances screening and early discrimination from health records.

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Computational biology
Medical informatics
Genomics

Background:

Rare diseases impact over 300 million globally, with diagnostic delays hindering timely treatment and research participation.
Current computational rare disease recognition (RDR) methods struggle with incomplete and heterogeneous knowledge resources, requiring extensive expert curation.
Direct application of large language models (LLMs) for diagnosis faces knowledge bottlenecks and raises concerns about cost, reproducibility, and data governance.

Purpose of the Study:

To introduce GEN-KnowRD, a novel framework that leverages LLMs to construct a computable knowledge base for rare disease recognition.
To evaluate GEN-KnowRD's performance in general-purpose rare disease screening and early discrimination using longitudinal electronic health records.
To demonstrate the benefits of a knowledge-layer-first approach in RDR, decoupling knowledge generation from patient-level inference.

Main Methods:

GEN-KnowRD utilizes LLMs to generate schema-guided rare disease profiles and assesses their quality.
A computable knowledge base, PheMAP-RD, is constructed for local deployment.
Lightweight inference pipelines integrate this knowledge for disease screening and early discrimination from electronic health records.

Main Results:

GEN-KnowRD significantly improved disease ranking in general-purpose screening benchmarks (up to 345.8% improvement vs. HPO-centered framework).
The framework outperformed advanced end-to-end LLM reasoning (up to 129.1% improvement) and expert-curated knowledge variants.
In a real-world idiopathic pulmonary fibrosis cohort, GEN-KnowRD showed robust gains in early discrimination performance.

Conclusions:

Repositioning LLMs to the knowledge layer enhances rare disease recognition (RDR) performance.
GEN-KnowRD provides a scalable, updatable, and reusable infrastructure for rare disease diagnosis, screening, and research.
This approach addresses limitations of existing RDR methods, offering improved accuracy and efficiency.