MEDICAL AI - AI for Multiplex Immunofluorescence in Histopathology скачать в хорошем качестве

MEDICAL AI - AI for Multiplex Immunofluorescence in Histopathology

MEDICAL AI — AI for Multiplex Immunofluorescence in Histopathology Thirty-one years ago, I had the privilege of working as an intern under the mentorship of one of the most distinguished pathologists, Dr. Methil Kannan Kutty—a legend in the league of Stanley Robbins, Ramzi Cotran, and Vinay Kumar. About ten years ago, histopathology made a leap with a new technique called multiplex immunofluorescence (mIF) akin to moving from a black-and-white photograph of a single person to a high-definition image of a crowded party. In the past, pathologists could focus on only one biological marker at a time. The arrival of multiplex immunofluorescence fundamentally changed this. Multiple markers could now be visualized simultaneously within the same tissue section, with all cells preserved in their original spatial context. This made it possible to identify different cell types and biomarkers, observe how they interact, and understand their collective roles—especially in complex environments such as tumors. However, this power comes at a steep cost. Multiplex immunofluorescence requires highly specialized and expensive microscopes, imaging systems, reagents, and tightly controlled laboratory environments. The process is slow and labor-intensive, resulting in low throughput and limited scalability. Fast forward to 2026. The advent of generative and multimodal AI is once again transforming histopathology. This video demonstrates a purpose-built large language model analyzing standard Hematoxylin-Eosin–stained images to generate microscopic examination findings and diagnostic insights. A vision-language model is then used to detect and infer multiple biomarkers directly from these images—without relying solely on complex laboratory workflows.