microscopy

Today in AI for life science: Biohub’s protein world model designs cancer binders that work in the wet lab, ’thinking microscopes’ turn instruments into co-scientists, and a sharp reminder that predictions still have to survive the bench.

At an invited talk at CZI, we ran a live, fully agent-controlled wet-lab experiment on the REEF Imaging Farm. A single natural-language prompt set an entire lab in motion, on real cells, in real time. We interviewed the agent that ran it.

Wei Ouyang and Hanzhao Zhang wrote the ‘Large Language Models and AI Agents’ chapter for AI in Microscopy: A BioImaging Guide (HHMI Janelia) — a tour from deep learning to autonomous, agent-driven bioimage analysis.

Today in AI for life science: an AI agent that drives ImageJ, the case for ’thinking microscopes’ that run their own experiments, and a foundation model that imagines how cells morph under perturbation.

Control microscopes in plain language — LLM agents that turn scientific intent into imaging, segmentation, and analysis.

A self-driving cell-biology lab where AI agents plan, run and adapt live-cell experiments across a cluster of instruments.

A framework that makes deep-learning pipelines for microscopy flexible, shareable, and reproducible across computing environments.

Building an AI-powered self-driving microscopy system for studying cellular response