bioimage-analysis

Today in AI for life science: a benchmark shows AI ‘scientists’ still struggle to even find the right papers, agents arrive in ImageJ with reproducibility built in, and the Human Cell Atlas convenes as the retina gets mapped.

Today in AI for life science: an AI system that autonomously discovers and validates a drug candidate, frontier labs buying into biology, a clear-eyed benchmark of cell-segmentation foundation models, and the case for composable multimodal models.

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.

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.

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.

A universal deep-learning approach for accurate FISH spot detection across imaging-based spatial-omics datasets.

The classic ImageJ, reimagined to run entirely in the web browser — no installation, full plugin support, and AI assistance.

A community-driven, AI-powered assistant that helps researchers navigate bioimaging tools, databases and services — and autonomously runs analysis tasks from plain-language prompts. Published in Nature Methods.