Rare Disease

AI-MARRVEL (AIM) is a knowledge-driven AI system for diagnosing Mendelian disorders that uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. It incorporates expert-engineered features to recapitulate the complex decision-making processes in molecular diagnosis. AIM doubled the rate of accurate genetic diagnosis across three independent real-world cohorts compared to benchmarked methods. Its confidence metric achieved 98% precision and identified 57% of diagnosable cases from 871 unsolved cases. AIM also demonstrated potential for novel disease gene discovery.

Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.

DeepRare is a multi-agent system for rare disease differential diagnosis that integrates large language models with structured medical knowledge to provide traceable reasoning. It aims to reduce the diagnostic odyssey for rare disease patients by leveraging AI agents to analyze clinical phenotypes, genomic data, and medical literature in a transparent, interpretable manner.

Rare diseases affect more than 300 million people worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged 'diagnostic odyssey' exceeding 5 years, marked by repeated referrals, misdiagnoses and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burden. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support with traceable reasoning.

A natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation for Mendelian diseases via structured tool access (MCP). MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. A 20B-parameter model achieved 94% pass rate on 100 expert-curated questions (vs 41% without MARRVEL-MCP), approaching state-of-the-art proprietary performance. Establishes context engineering as a core principle for biomedical AI.

Variant interpretation in rare diseases requires navigating multiple genomic databases, each with strict input formats, while synthesizing heterogeneous evidence. To address these usability barriers, we developed MARRVEL-MCP, a natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation via structured tool access. MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. Using 100 expert-curated questions, lightweight models (3B-20B parameters) with MARRVEL-MCP matched or outperformed larger models without tool access. A 20B-parameter model achieved a 94% pass rate, versus 41% without MARRVEL-MCP, approaching state-of-the-art proprietary performance. These findings establish context engineering as a core principle for biomedical AI and support scalable integration of LLMs with curated genomic resources.