Spatial skills develop early in life. We catch balls, pour drinks, ride escalators, and perform countless activities that require spatial awareness. What would happen if machines could emulate this 3D understanding of the world? University of Virginia assistant professor Lei Li is exploring this new frontier of AI at the University of Virginia's Spatial AI Lab.

Researchers at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan have developed a score that predicts the risk of liver cancer. The study establishes that the protein MYCN drives liver tumorigenesis, specifically of the type of tumors found in the deadliest subtype of liver cancer. The study characterizes the microenvironment of genes that permit overexpression of MYCN, and describes a machine-learning algorithm that uses this data to predict how likely a tumor-free liver is to develop tumors.

Researchers from the Indian Institute of Technology Gandhinagar presented a scalable, automated framework for pollen identification using scanning electron microscopy images and machine learning. The team developed an open-access web-based application that organises pollen images and associated data. The computer vision framework provides a reproducible, adaptable solution for pollen analysis, improving speed and accuracy while minimising reliance on manual annotation. The study involves an interdisciplinary methodology with broad applications in domains ranging from plant taxonomy and biodiversity monitoring to agriculture, pollen allergies, and paleoecology.

JMIR Publications invites submissions to a new theme issue titled “Bridging Data, AI, and Innovation to Transform Health” in its open access journal JMIR Bioinformatics and Biotechnology.