Rare Disease Diagnosis

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Rare disease diagnosis connects a patient's symptoms and test results to conditions that may be unfamiliar or have overlapping features. Common approaches include standardizing symptoms with the Human Phenotype Ontology (HPO), ranking candidate diseases or genes with ontology and knowledge-graph methods, and using language models to extract and reason over clinical notes; some systems also combine genetic data with facial images. Researchers are testing active phenotype questions, multimodal agents, and synthetic images or records to address sparse data, while benchmarks assess diagnostic ranking, evidence quality, and performance on real clinical cases. A central challenge is making systems accurate across rare and underrepresented conditions while keeping their evidence verifiable, their outputs useful to clinicians, and patient data private.

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An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Feb 23, 2026Cathy Shyr, Yan Hu, Rory J. Tinker +8Clinical Information Extraction Rare Disease Diagnosis
Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, 37203, TN, USA. · Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, 37203, TN, USA. · Department of Pediatrics, Vanderbilt University Medical Center, 2200 Children’s Way, Nashville, 37232, TN, USA. +5
GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text
Dec 23, 2023Da Wu, Zhanliang Wang, Hongzhuo Chen +12Rare Disease Diagnosis Multimodal Classification
Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA · Department of Mathematics, University of Pennsylvania, Philadelphia, PA 19104, USA · Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA +8

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An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Cathy Shyr, Yan Hu, Rory J. Tinker, Thomas A. Cassini+7

Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, 37203, TN, USA. · Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, 37203, TN, USA. · Department of Pediatrics, Vanderbilt University Medical Center, 2200 Children’s Way, Nashville, 37232, TN, USA.+5

Clinical Information Extraction Rare Disease Diagnosis Medical LLMs

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Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontology (HPO) terms, and prioritizing diagnostically informative HPO terms. We developed RARE-PHENIX, an end-to-end AI framework for rare disease phenotyping that integrates large language model-based phenotype extraction, ontology-grounded standardization to HPO terms, and supervised ranking of diagnostically informative phenotypes. We trained RARE-PHENIX using data from 2,671 patients across 11 Undiagnosed Diseases Network clinical sites, and externally validated it on 16,357 real-world clinical notes from Vanderbilt University Medical Center. Using clinician-curated HPO terms as the gold standard, RARE-PHENIX consistently outperformed a state-of-the-art deep learning baseline (PhenoBERT) across ontology-based similarity and precision-recall-F1 metrics in end-to-end evaluation (i.e., ontology-based similarity of 0.70 vs. 0.58). Ablation analyses demonstrated performance improvements with the addition of each module in RARE-PHENIX (extraction, standardization, and prioritization), supporting the value of modeling the full clinical phenotyping workflow. By modeling phenotyping as a clinically aligned workflow rather than a single extraction task, RARE-PHENIX provides structured, ranked phenotypes that are more concordant with clinician curation and has the potential to support human-in-the-loop rare disease diagnosis in real-world settings.

Latest papers

An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Text