AI

This week in AI for life science: Anthropic bets on agent infrastructure over bigger models, the virtual cell meets honest benchmarks, self-driving labs go mainstream, and spatial omics gets its foundation models — plus a new chapter from the lab.

Our BioEngine preprint is out on bioRxiv. It turns the explosion of community AI models into something a biologist — or an AI agent — can actually run, on a laptop or an HPC cluster, no code required.

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.

Can genuine scientific discovery emerge from radically simple AI agent loops? We explore autonomous research agents built from minimal ingredients.

An agent-first platform that deploys bioimage AI models as autoscaling, agent-readable interfaces on cloud or HPC — and powers the “test run” feature on the BioImage Model Zoo. Described in a 2026 preprint.

A community-driven, fully open repository where standardized deep-learning models can be shared, tested in the browser, and deployed across imaging tools.

Open-source framework that connects AI models, data, and instruments into shared workspaces — the backbone of the lab’s platforms.

An AI assistant that helps scientists find the right imaging technologies and facilities across the Euro-BioImaging network from plain-language questions.

A distributed platform for deploying and adapting bioimage AI models as agent-readable interfaces, with autoscaling and FAIR data access — the engine behind the BioImage Model Zoo test runs.

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