Tekpak Automation will demonstrate the benefits of robotics for manufacturers across the food, beverage and pharmaceutical industries at interpack 2026, with a live working demonstration of a pick-and-place cell on Stand A15/Hall 16. Tekpak is known for solving complex packaging line challenges with proven, modular automation. Built on over 25 years’ experience supporting pharmaceutical and […]
Vecna Robotics, a provider of flexible material handling automation, has announced CaseFlow Voice, which the company says is the only case picking automation solution with fully integrated voice technology. CaseFlow Voice embeds Lucas Systems’ “Jennifer” voice capabilities directly into case picking workflows, enabling hands-free operations that improve worker productivity and accuracy while helping warehouses achieve […]
Autonomous systems are designed for repetition. They are good in the situations where patterns can be memorized, charted, and anticipated with a high level of certainty. However, real-world driving is filled with edge cases, which do not scale well to datasets. Even a sophisticated system can be thrown off by a plastic bag floating along […]
Researchers at Kobe University have developed an AI system that can detect acromegaly, a rare hormone disorder, by analyzing photos of the back of the hand and a clenched fist. The disease often develops slowly and can take years to diagnose, even though untreated cases may shorten life expectancy.
Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.
Researchers at the University of Michigan have created an AI system that can interpret brain MRI scans in just seconds, accurately identifying a wide range of neurological conditions and determining which cases need urgent care. Trained on hundreds of thousands of real-world scans along with patient histories, the model achieved accuracy as high as 97.5% and outperformed other advanced AI tools.