ArticleEuropean journal of human genetics : EJHG2026
Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.
Article in European journal of human genetics : EJHG, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Systematic identification of rare disease patients in electronic health records enables evaluation of clinical outcomes.Scientific reports · 2026Article
- Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases.EBioMedicine · 2025Article
- Accuracy of Large Language Models in Generating Rare Disease Differential Diagnosis Using Key Clinical Features.Studies in health technology and informatics · 2025Article
- Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Comparative analysis of large language models and traditional diagnostic decision support systems for rare rheumatic disease identification.EULAR rheumatology open · 2025Article
- Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 Cases.medRxiv : the preprint server for health sciences · 2025Article
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Abstract
Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses, and their accuracy compared to existing diagnostic tools is not well characterized. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5213 previously published case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to seven LLMs, including four generalist models and three LLMs specialized for medical applications. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.
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