Artificial intelligence models that automatically label organs and anatomical structures in medical images are increasingly used to study large public imaging datasets, but comparing these tools is difficult when no expert reference annotations exist. A recent study developed an open-source framework for evaluating and comparing AI-based anatomy segmentation models using inter-model agreement rather than ground truth. Applying the approach to six widely used segmentation models and chest CT scans from the National Lung Screening Trial, the researchers normalized all model outputs to follow standard lexicon, and stored the result in a common medical imaging format. Normalized representation made it possible to visualize and compare model outputs, and analyze how consistently each model segmented key structures such as the lungs, heart, ribs, vertebrae, and sternum. The results show strong agreement across models for lung segmentation, but substantial inconsistencies and systematic errors for bones and some cardiac structures, particularly among models trained on similar data. By combining quantitative agreement metrics with interactive visualization tools, the framework helps identify reliable models and flag problem cases, offering a practical way to guide model selection and large-scale reuse of AI-generated annotations.

Six linked studies show how state-coordinated media in AI training data influences model responses about politics, especially in a country’s own language.

A scientific team consisting of roboticists from Delft University of Technology and biologists from Wageningen University (both The Netherlands) and Carl von Ossietzky University of Oldenburg (Germany) present: “Bee-Nav”, a robot navigation strategy inspired by honeybees. It allows even very small robots to travel far away from home and return successfully, using a neural memory of only 42 kilobytes. In a new environment, the robot first performs a short learning flight near home, just as honeybees do. After that, it can travel away for hundreds of meters and still find its way back. Bee-Nav enables lightweight, safe robots to navigate on their own, opening the door to applications such as butterfly-like drones monitoring greenhouses. The research also offers new insight into how flying insects may find their way home.

The human eye is not merely an optical window to the world, but also a "micro-display" of systemic microcirculation and neural activity. In the current era of big data, the rapid expansion of multi-source and multimodal ophthalmic datasets presents unprecedented opportunities. A critical scientific question emerges: how can Artificial Intelligence (AI) unlock the hidden potential embedded within these vast and heterogeneous datasets?

Recently, a comprehensive review titled "Data-driven computational methods in ophthalmology: A multimodal perspective" was published in the international journal Eye Discovery. From a "data-centric" perspective, the article systematically evaluates the scientific value of multimodal ophthalmic data and analyzes cutting-edge advances and future challenges in AI-driven ophthalmic research.