A groundbreaking study has successfully sequenced B and T cell receptors (BCR and TCR) to create the first diagnostic immunome, using AI-powered models to classify autoimmune diseases with remarkable accuracy. The Mal-ID (Machine Learning for Immunological Diagnosis) system analyzed immune responses across diseases like lupus, Type 1 diabetes, and HIV, achieving up to 99% accuracy. This marks a major leap in diagnosing and potentially predicting autoimmune conditions before symptoms appear. The study leveraged machine learning and large language models (LLMs), such as Evolutionary Scale Model-2 (ESM-2), to analyze immune system data. Despite some limitations—such as a retrospective design and a limited number of diseases tested—the results offer a first proof-of-concept for large-scale immunome diagnostics. Researchers believe this could soon become a routine medical assessment, improving precision medicine, disease prevention, and longevity forecasting. https://open.substack.com/pub/erictopol/p/the-first-diagnostic-immunome