Addressing the global challenge of diagnosing rare diseases, scientists in Shanghai have unveiled DeepRare, the world's first intelligent evidence-based reasoning diagnostic system specifically designed for rare diseases, which outperforms traditional medical AI across multiple dimensions.

A paper on the innovative system, developed by a joint team from Shanghai Jiao Tong University's School of Artificial Intelligence and Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, was published on the website of the journal Nature on Thursday.

The system integrates vast medical literature databases with real-time clinical data, enabling a more sophisticated approach to diagnosis. Unlike conventional AI, which relies on rapid pattern matching, DeepRare mimics the "slow thinking" of human doctors. It can proactively ask questions to fill in missing information and meticulously refine diagnostic information through a cycle of hypothesis, verification and self-reflection, helping to address potential logical flaws.

Researchers said every diagnostic conclusion generated by the system is traceable and accompanied by a clear and complete evidence chain, allowing doctors to understand not only what the diagnosis is, but also why it was made.

According to the research, when provided with only clinical phenotype information without genetic data, DeepRare achieved a top-ranked diagnostic accuracy of 57.18 percent, representing a 23.79 percentage point improvement over the previous best international model. Scientists said such a system could serve as a powerful tool for rapid screening in primary care settings.

The performance of the system is further enhanced when genetic sequencing data is introduced. With the support of multimodal data, DeepRare's comprehensive top-ranked diagnostic accuracy in complex cases exceeds 70.6 percent, significantly outperforming the widely used international tool, Exomiser, which stands at 53.2 percent.