A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial

RaDaR (Rare Disease navigatoR), a new open-source reasoning large language model with 32 billion parameters, was developed to enhance the diagnosis of rare diseases. It was trained on a dataset of 49,170 real cases and 104,666 synthetic cases, demonstrating superior performance over larger models like DeepSeek-R1 in public benchmarks and clinical validations. RaDaR's integration into clinical practice improved diagnostic accuracy by 21.44 percentage points in a randomized trial, highlighting its potential to significantly reduce lead times in rare disease diagnosis, thus offering a valuable tool for practitioners facing data scarcity in this domain.