'Agentic' AI system delivers high accuracy in rare disease diagnosis

A new artificial intelligence (AI) system designed to diagnose rare diseases has shown it can match – and in some cases surpass – existing clinical tools, according to a research published on Wednesday in Nature.

The system, called DeepRare, aims to address a long-standing problem in medical industry: patients with rare conditions often spend years seeking a correct diagnosis. 

The study was conducted by a joint team from Shanghai Jiao Tong University's School of Artificial Intelligence and Xinhua Hospital affiliated with its School of Medicine. At Xinhua Hospital, DeepRare is currently undergoing internal testing, with plans to support doctors in diagnostic cases. 

In tests using only patients' clinical phenotype information – similar to a doctor making an initial judgment based on symptoms alone – DeepRare achieved a Recall@1 of 57.18 percent, meaning the first diagnosis was correct more than half of cases. The performance suggests the tool could support screening in hospitals that lack routine access to genetic testing.

When genomic sequencing data were added, DeepRare's Recall@1 rose to 70.6 percent in complex cases, outperforming Exomiser, a widely used international tool for genetic analysis, which recorded 53.2 percent under comparable conditions.

A survey by the China Alliance for Rare Diseases covering more than 20,000 patients found that 42 percent had previously been misdiagnosed, and patients waited an average of 4.26 years before receiving a confirmed diagnosis.

Unlike traditional medical AI systems that primarily match symptoms to disease categories, DeepRare follows what the researchers describe as an "agentic" workflow. Like a human doctor, it forms hypotheses, tests them against evidence and revises its conclusions before ranking possible diseases.

The system has already been deployed on an online diagnostic platform since July 2025, with more than 600 medical institutions worldwide registered. The research team plans to launch a global rare disease diagnostic alliance and further validate the system using 20,000 real-world cases in the coming months.