Doctors and scientists have long used microscopes to examine human tissue and diagnose disease. However, modern medical research now generates vast amounts of complex data—such as detailed maps of genes and proteins within cells—that exceed the capacity of the human eye to interpret. A new study from Yale University demonstrates how artificial intelligence can integrate these diverse data types, creating a more comprehensive view of cellular activity within diseased tissues. By combining spatial information from tissue samples with molecular data, the AI tool helps researchers understand how cells interact in real time, revealing new insights into disease mechanisms. The approach enables scientists to see not just individual cells, but how they work together in networks, offering a deeper understanding of conditions like cancer, autoimmune disorders, and neurodegenerative diseases. The findings, published in Nature Biomedical Engineering, highlight a powerful new way to bridge the gap between microscopic observations and molecular-level data, paving the way for more accurate diagnoses and targeted therapies.