Imagine diagnosing diseases with unprecedented accuracy, all thanks to AI that understands the nuances of human language. That's the promise of a groundbreaking new technique using large language models (LLMs) to revolutionize pathology image analysis. Traditional methods often struggle with the intricate details and subtle variations present in these images. This new research tackles this challenge head-on by combining the visual prowess of AI with the descriptive power of text prompts. Think of it like giving the AI a magnifying glass and a set of instructions. Instead of relying on pre-defined categories, pathologists can use natural language to specify exactly what they want the AI to analyze, whether it's a specific type of cell, a functional unit, or even the layers within a tissue sample. This method, called Prompt-guided Flexible Pathological Segmentation (PFPs), lets the AI adapt to different tasks dynamically, significantly boosting its flexibility and precision. Researchers tested this innovative approach on a kidney pathology dataset, using a variety of free-text prompts to guide the AI's segmentation process. The results? The AI demonstrated a remarkable ability to understand and execute complex instructions, even generalizing to unseen cases. While still in its early stages, this research opens exciting new avenues for medical image analysis. By bridging the gap between human language and AI interpretation, PFPs has the potential to transform pathology, leading to more accurate diagnoses and ultimately, better patient outcomes. The future of pathology is here, and it speaks our language.