For most of my career, the hardest part of research wasn’t running experiments. It was making sense of the results. When I started studying autoimmune disease, a single experiment might have produced a few dozen data points. Today, one experiment can produce millions. AI, is changing how we handle that flood of information. As someone who studies how the immune system turns against the body, I’m excited about these tools. I’m also careful about them. Here is an honest look at both sides.
Why Immunology Needs Help With Data
Your immune system is one of the most complex systems in nature. It contains billions of cells, and many of them carry unique receptors that recognize specific targets, like a lock and key. In autoimmune diseases like lupus, rheumatoid arthritis, type 1 diabetes, and multiple sclerosis, some of these immune cells mistakenly attack healthy tissue. Figuring out which cells go wrong, and why, means tracking huge numbers of cells, genes, and proteins at once.
Modern tools such as single-cell RNA sequencing let us measure thousands of genes inside each of hundreds of thousands of individual cells. No human can scan that much information by hand. This is where AI shines.
What Does AI Do Well?
Finding patterns in big datasets. AI programs, especially a type called machine learning, are very good at spotting patterns. They can sort cells into groups, flag rare cell types, and notice which genes tend to switch on together. In my field, this has helped researchers identify small populations of immune cells that seem to drive disease flares.
Predicting protein shapes. A protein’s shape determines what it does. For decades, figuring out a single protein’s structure could take years of lab work. AI tools like AlphaFold can now predict many protein structures in minutes. The importance of this work was recognized with the 2024 Nobel Prize in Chemistry. For immunologists, better structure predictions help us understand how antibodies and immune receptors grab onto their targets.
Narrowing down drug and vaccine targets. AI can screen large libraries of molecules and predict which ones might block a harmful immune signal. It can also predict which small pieces of a virus, or of our own proteins, the immune system is likely to recognize.
This doesn’t replace experiments, but it helps scientists decide which experiments are worth doing first.
Handling the paperwork of science. AI language tools can summarize research papers, help write computer code for data analysis, and draft sections of reports. These tasks used to eat up hours of a scientist’s week. Saving that time means more time for thinking and experimenting.
What Can’t AI Do?
It can’t replace experiments. An AI prediction is a hypothesis, not a fact. A program may predict that a drug will calm an overactive immune response, but only lab testing and clinical trials can show whether it is safe and effective in real people. Biology is full of surprises that computers don’t anticipate.
It only knows what it was trained on. AI learns from existing data. If that data is incomplete or biased, the AI will be too. For example, many large medical datasets include mostly people of European ancestry. Autoimmune diseases affect different groups in different ways. Lupus, for instance, is more common and often more severe in Black, Hispanic, and Asian women. An AI trained on unbalanced data could miss these differences and lead us to the wrong conclusions.
It struggles to explain itself. Many AI systems are like “black boxes.” They give an answer but can’t clearly show how they got there. In science and medicine, the “why” matters. If we don’t understand the reasoning, it’s hard to trust the result or build on it.
It can make things up. AI chatbots sometimes produce statements that sound confident but are false. They have even invented research papers that don’t exist. Every fact an AI provides must be checked against real sources.
It lacks judgment and creativity. AI does not wonder. It doesn’t notice something odd under a microscope and ask, “What if?” The biggest breakthroughs in immunology came from curious people asking unexpected questions. AI can help answer questions, but it still depends on humans to ask good ones.
The Road Ahead
I think of AI as a powerful new lab partner. It is fast, tireless, and great at tactical analysis, but it needs supervision. The best research will come from teams that combine AI’s speed with human expertise, careful experiments, and ethical responsibility. For patients living with autoimmune disease, that partnership could mean faster diagnoses, better treatments, and a deeper understanding of why the immune system sometimes loses its way.
The future of immunology won’t be written by machines alone. It will be written by scientists who know how to use them wisely.
Dr. Gupta has a PhD in biomedical sciences from University of Florida College of Medicine and is an AI Regulatory Solutions Specialist at Weave Bio.
