| Sumario: | This article examines the ethical and epistemic implications of using artificial intelligence (AI) in research on ultra-rare diseases, where patient populations are extremely small and conventional clinical trials are often infeasible. It highlights AI’s role as an infrastructure of visibility that can identify eligible patients across fragmented data sources, potentially improving diagnosis and access to experimental therapies, but also risks reinforcing existing inequities due to uneven data representation. The article discusses AI-generated synthetic evidence, such as digital twins and synthetic control arms, as emerging tools for knowledge generation that raise questions about reliability and standards of evidence in ultra-rare disease research. It emphasizes the importance of patient and community involvement in governing AI use, advocating for transparency and stewardship to ensure equitable distribution of research opportunities and trust in AI-enabled systems.
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