Use of synthetic data, a novel paradigm for immunopathology

The complexity and heterogeneity of autoimmune diseases are only partially captured by current analytic tools, even when deep learning techniques are employed to intercept patterns beyond existing dogma. Synthetic data offer a newer paradigm through machine-generated reconstructions of real-world data that faithfully attempt to recapitulate biological and clinical patterns without creating duplicates and maintaining the privacy of the original ones. Synthetic data act as a magnifying lens, allowing predictions otherwise not possible on disease classification, progression, and therapeutic response. This approach has several advantages and is currently underutilized. Firstly, it provides cohort enrichment and equilibrates group imbalances. Second, it generates synthetic arms for both in vitro studies and human clinical trials, relevant to disentangle the rarity and heterogeneity of autoimmune diseases. Third, the platform allows applications beyond tabular registries, including medical images, genomics, and flow cytometry data. Last, 'digital twins' act through dynamic bidirectional links with the biological/clinical system counterpart, lending themselves to transformative opportunities for precision medicine. Herein, we discuss the current status of this fast-moving novel component of artificial intelligence and its implications for autoimmune diseases.