Leveraging Generative AI for Precision Medicine: Interpreting Immune Biomarker Data from EHRs in Autoimmune and Infectious Diseases

Abstract

This study reviews the interpretation of immune biomarkers by generative artificial intelligence regarding precision medicine. Precision medicine customizes diagnostics and therapy based on various individual characteristics of patients, especially genetic and immune biomarkers in autoimmune and infectious diseases. Generative AI manages these in much-simplified ways through real-time decision-making in the improved clinical outcome by analyzing a complex Electronic Health Records system. During PRISMA guidelines, the systematic review published 655 articles filtered to study 40 articles pertaining to generative AI in immune biomarker analysis and electronic health records. These studies used machine learning (ML), generative adversarial networks (GANs), transformers, and large language models (LLMs). Generative AI makes a stride ahead in real-time biomarker analysis to predict risk, efficacy in treatment, and vaccine design for diseases such as lupus, rheumatoid arthritis, sepsis, and COVID-19. The challenges are data inconsistency, ethical matters, and AI interpretability. It is of paramount importance to improve data standardization, systematized AI as application, interdisciplinary collaboration, and great enhancement in way of effective application in generative AI with precision medicine.