As new gene-disease relationships, case reports, and classification evidence accumulate, unsolved cases can become newly interpretable.

Researchers from Boston Children’s Hospital’s Manton Center for Orphan Disease Research, Harvard University, and OpenAI used the OpenAI o3 Deep Research reasoning model to analyze de-identified clinical and genomic information from 376 previously analyzed cases that remained unsolved. The model surfaced evidence-linked candidate explanations for researchers and clinicians to review. Following expert review, additional testing, and clinical confirmation, physicians established diagnoses in 18 cases—an additional diagnostic yield of 4.8% after earlier analysis by specialists. This study was published on June 18, 2026, in NEJM AI and shows how an AI-assisted research workflow can help experts generate leads when revisiting some of the most difficult cases.

Many of these cases had evaded years of expert analysis. In this study, OpenAI o3 Deep Research helped researchers identify leads that were later assessed through established clinical processes, suggesting that expert-led periodic reanalysis could become more scalable as knowledge evolves. The model did not diagnose any patient or make any clinical decision. It produced evidence-linked hypotheses for specialists to review and, where appropriate, investigate through additional testing and confirm in a clinical laboratory.

An inconclusive genetic test is not always a permanent finding. A patient’s phenotype descriptions, test results, and family history can be split across databases that use different identifiers, formats, and vocabularies. Linking those records is difficult, so even specialists can miss a diagnosis. Experts may also sequence a child’s genome before a relevant gene or its variants have been linked to disease. As scientific knowledge advances, the same data can reveal answers that were previously impossible to uncover.