The immune health metric (IHM) developed by John Tsang and colleagues represents a significant advancement in immunology by providing a detailed measurement of immune system health. Unlike conventional tests such as complete blood counts, which quantify a limited array of immune cells, the IHM integrates data from millions of immune-related cells, proteins, and mRNA, analyzed through machine learning and AI to yield a numerical score of immune robustness. This technology captures the complex and dynamic nature of an individual’s immunome shaped by genetics, past exposures, and environmental factors.
The implications of this work are profound for clinical medicine. The IHM can identify immune disruptions common across various diseases, potentially flagging health issues before symptoms emerge. It may explain variability in responses to infections and vaccines, as seen during the COVID-19 pandemic. However, challenges remain including the need for large-scale, geographically diverse data to account for environmental and genetic differences. Projects like the Human Immunome Project aim to globally expand immunome profiling to address these gaps.
Long-term, integrating IHM testing into routine physical exams could transform healthcare by offering personalized immune monitoring and guiding targeted therapies. Researchers anticipate further refinement of tests to assess specific immune functions and risks, ultimately enhancing disease prediction, prevention, and treatment. The technology is still emerging, requiring additional validation and standardization before widespread clinical adoption.
👉 Pročitaj original: MIT Technology Review – AI
