Real-World Data in Autoimmune Disease Research

OneMedNet recently announced a partnership with ViuHealth to expand the amount of longitudinal autoimmune disease data available through its real-world data platform.

The collaboration is expected to capture ongoing patient information, including symptom patterns, flare activity, treatment use, and outcomes.

The companies state that the expanded dataset may be used by life sciences and artificial intelligence (AI) groups to analyze treatment patterns, identify patient subgroups, and generate real-world evidence.

This reflects a broader shift in autoimmune research toward combining traditional clinical trials with real-world data collected from routine care.

Clinical trials remain essential for evaluating safety and effectiveness under structured protocols. Real-world data, however, may provide longer-term insight into how chronic autoimmune conditions fluctuate and how patients respond to therapy outside tightly defined study criteria.

As datasets grow, AI tools are increasingly used to analyze large volumes of health data to detect patterns that might not be visible in smaller studies.

In a recent review published in the Journal of Investigative Dermatology, researchers describe how machine learning is already being applied to inflammatory and autoimmune-related skin diseases, including atopic dermatitis, psoriasis, lupus, and scleroderma. Models trained on clinical images, pathology slides, gene expression data, and electronic health records are being explored to improve disease classification, identify molecular subtypes, and guide more personalized treatment strategies*. Computational approaches are also being used to prioritize drug targets and evaluate potential therapy repurposing.

*However, the authors note important limitations, including small or nonrepresentative training datasets, bias in underrepresented populations, inconsistent external validation, and challenges integrating predictive tools into routine care.

Expanded use of real-world patient data and AI analytics underscores the need for strong privacy protections, transparent governance, and ethical oversight as these tools continue to evolve.

Citations

OneMedNet Corporation. (2026, February 26). OneMedNet partners with ViuHealth to enhance autoimmune dataset scale, accelerating recurring revenue from life sciences and AI customers. https://www.onemednet.com/news/onemednet-partners-with-viuhealth-to-enhance-autoimmune-dataset-scale-accelerating-recurring-revenue-from-life-sciences-and-ai-customers/

Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026). Artificial intelligence-enabled precision medicine for inflammatory skin diseases. The Journal of investigative dermatology, S0022-202X(25)03518-3. Advance online publication. https://doi.org/10.1016/j.jid.2025.10.596