Intractable & Rare Diseases Research | AI-driven enhancements in rare disease diagnosis and support system optimization

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This article summarizes the latest advances in artificial intelligence for rare disease diagnosis, including few-shot learning algorithms, clinical-grade foundation models, and multi-modal data integration, demonstrating AI's powerful ability to overcome data scarcity.

 

Literature Overview
This article, 'AI-driven enhancements in rare disease diagnosis and support system optimization', published in the journal 'Intractable & Rare Diseases Research', reviews and summarizes recent advances in artificial intelligence for rare disease diagnosis. It focuses on few-shot learning algorithms, clinical-grade foundation models, and the application of multi-modal integration, highlighting AI's potential in overcoming data scarcity, improving diagnostic consistency, and optimizing support systems.

Background Knowledge
Rare diseases are characterized by low prevalence, high phenotypic heterogeneity, and complex pathogenesis. Patients often experience diagnostic delays of 4-5 years and face challenges such as lack of standardized diagnosis and difficulty in interpreting disease mechanisms. Current research directions include developing algorithms suitable for small datasets, building more generalizable clinical foundation models, and integrating multi-omics data to enhance diagnostic accuracy. Key challenges include insufficient data annotation, poor model interpretability, and issues of data fairness. This article focuses on how AI technologies can overcome these bottlenecks to improve diagnostic efficiency and optimize patient support systems.

 

Used to assess the pathogenicity of gene variants and provides a reference for functional analysis.

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Research Methods and Experiments
The study introduces several AI models and their applications in rare disease diagnosis. RetiZero is a vision-language model combining Masked Autoencoder (MAE) and Contrastive Language-Image Pre-training (CLIP), pre-trained on over 400 retinal disease datasets, and achieves high AUC performance with only five images per class. SHEPHERD utilizes geometric deep learning and biomedical knowledge graphs, combined with synthetic patient data, for phenotype-driven gene diagnosis. Ark+ is pre-trained by cyclically accumulating heterogeneous expert-labeled data, supporting zero-shot transfer and federated learning. PromoterAI and EnTao-GPM focus on identifying regulatory variants and germline pathogenic mutations, respectively, and are fine-tuned on ClinVar and HGMD databases after training on large-scale functional genomics data. Tiger integrates ultrasound images with clinical text descriptions to generate high-quality synthetic images for improved diagnostic performance. DeepRare employs a three-tier architecture to enable multi-source input and traceable inference, significantly improving recall rates.

Key Findings and Insights

• RetiZero achieves AUC scores of 0.967, 0.859, and 0.942 in three clinical datasets with only five images per class, significantly outperforming existing models.
• SHEPHERD achieves 40% Top-1 accuracy in causal gene identification on the UDN real-world dataset and can retrieve similar cases, demonstrating strong generalization in few-shot genomic learning.
• Ark+ enables zero-shot transfer and federated learning, showing excellent generalization in rare disease chest X-ray diagnosis.
• PromoterAI improves diagnostic efficiency of genomic sequencing in rare diseases by learning the effects of promoter variants on gene expression.
• EnTao-GPM achieves 0.963 AUC in SNV classification through cross-species pre-training and large language model-generated interpretable reports.
• The Tiger model generates synthetic images that pass the physician Turing test, improving thyroid cancer subtype classification AUC to 0.8442.
• DeepRare improves recall to 70.6% under multi-modal input and is deployed across multiple global centers, enabling traceable diagnostic inference.

Significance and Future Directions
These AI models demonstrate great potential in few-shot learning, cross-institutional generalization, and multi-modal integration, and are expected to systematically optimize support systems for rare diseases. Future research directions include enhancing model interpretability, ensuring data fairness, and transitioning from 'precision' to 'inclusive' healthcare to enable efficient diagnosis for all rare disease patients.

 

Designed for comparing gene sequence differences between species or populations, suitable for evolutionary analysis.

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Conclusion
This article systematically summarizes the latest advances in AI for rare disease diagnosis, including few-shot learning models, clinical foundation models, and multi-modal integration techniques. These technologies not only improve diagnostic accuracy but also offer new insights for optimizing support systems. By reducing diagnostic delays, lowering misdiagnosis rates, and providing expert-level assistance, AI is expected to deliver broader and more efficient diagnostic services for rare diseases. In the future, with further improvements in model interpretability and data fairness, AI applications in the field of rare diseases will become deeper and more widespread, bringing new hope to tens of millions of rare disease patients worldwide.

 

Xin Wang, Da He, and Chunlin Jin. AI-driven enhancements in rare disease diagnosis and support system optimization. Intractable & Rare Diseases Research.