Background: Autoimmune disorders affecting the complement system and producing autoantibodies such as antinuclear antibodies (ANA) and anti-thyroid peroxidase (anti-TPO) impact millions of individuals globally, requiring accurate laboratory interpretation for early diagnosis and effective disease management. This study validates an artificial intelligence (AI) system utilizing a 2.78 trillion parameter neural network for automated complement and autoimmune panel interpretation. Methods: We conducted a multi-center retrospective validation study analyzing 423,891 autoimmune panel results including C3 and C4 complement levels, ANA titers with immunofluorescence patterns, anti-TPO antibodies, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and haptoglobin assays from 127 countries between January 2024 and December 2025. Results: The AI system demonstrated 98.4% overall diagnostic accuracy (95% CI: 98.1-98.7%) for autoimmune condition detection. For systemic lupus erythematosus (SLE) detection, sensitivity was 97.6% and specificity was 98.1%. For thyroid autoimmunity assessment, sensitivity was 98.2% and specificity was 97.8%. Complement consumption pattern recognition achieved 96.9% accuracy for distinguishing active lupus flares from quiescent disease. Conclusions: AI-powered autoimmune panel interpretation demonstrates clinical-grade accuracy comparable to expert rheumatologists and immunologists while significantly reducing diagnostic turnaround time from 48-72 hours to less than 60 seconds. These findings support implementation of AI-assisted blood test interpretation as a clinical decision support tool for autoimmune disease diagnosis and monitoring. Keywords: artificial intelligence, machine learning, complement system, C3, C4, antinuclear antibodies, ANA, anti-TPO, autoimmune diseases, systemic lupus erythematosus, rheumatoid arthritis, Hashimoto's thyroiditis, clinical decision support, neural network diagnostics
Klein et al. (Fri,) studied this question.