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Category: robotics/AI – Page 150

Routine AI assistance may lead to loss of skills in health professionals who perform colonoscopies

The introduction of artificial intelligence (AI) to assist colonoscopies is linked to a reduction in the ability of endoscopists (health professionals who perform colonoscopies) to detect precancerous growths (adenomas) in the colon without AI assistance, according to a paper published in The Lancet Gastroenterology & Hepatology.

Colonoscopy enables detection and removal of adenomas, leading to prevention of bowel cancer. Numerous trials have shown the use of AI to assist colonoscopies increases the detection of adenomas, generating much enthusiasm for the technology. However, there is a lack of research into how continuous use of AI affects endoscopist skills, with suggestions it could be either positive, by training clinicians, or negative, leading to a reduction in skills.

Author Dr. Marcin Romańczyk, Academy of Silesia (Poland), says, To our knowledge, this is the first study to suggest a negative impact of regular AI use on health care professionals’ ability to complete a patient-relevant task in medicine of any kind.

Tiny robots use sound to self-organize into intelligent groups

Animals like bats, whales and insects have long used acoustic signals for communication and navigation. Now, an international team of scientists has taken a page from nature’s playbook to model micro-sized robots that use sound waves to coordinate into large swarms that exhibit intelligent-like behavior.

The robot groups could one day carry out complex tasks like exploring disaster zones, cleaning up pollution, or performing from inside the body, according to team lead Igor Aronson, Huck Chair Professor of Biomedical Engineering, Chemistry, and Mathematics at Penn State.

“Picture swarms of bees or midges,” Aronson said. “They move, that creates sound, and the sound keeps them cohesive, many individuals acting as one.”

CrowdStrike, Uber, Zoom Among Industry Pioneers Building Smarter Agents With NVIDIA Nemotron and Cosmos Reasoning Models for Enterprise and Physical AI Applications

As enterprises develop AI agents to tackle complex, multistep tasks, models that can provide strong reasoning accuracy with efficient token generation enable intelligent, autonomous decision-making at scale.

NVIDIA Nemotron is a family of advanced open reasoning models that use leading models, NVIDIA-curated open datasets and advanced AI techniques to provide an accurate and efficient starting point for AI agents.

Soft robots go right to the site of kidney stones

An international research team led by the University of Waterloo is developing technology to dissolve painful kidney stones in the urinary tract using tiny robots. The research is published in the journal Advanced Healthcare Materials.

The new technique, tested in a life-size, 3D-printed model, features thin, spaghetti-like strips fitted with magnets that can be moved into place near uric acid with a operated by doctors.

The soft, flexible robot strips are about a centimeter long and contain an enzyme called urease. Once in place, the urease reduces the acidity of the surrounding urine, thereby dissolving stones until they are small enough to pass naturally in just a few days.

AI automatically designs optimal drug candidates for cancer-targeting mutations

Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs) that could bind to that protein and block its function. This process is costly, time-consuming, and has a low success rate.

KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug candidates without any prior molecular data—opening up new possibilities for . The research is published in the journal Advanced Science.

The research team led by Professor Woo Youn Kim in the Department of Chemistry has developed an AI model named BInD (Bond and Interaction-generating Diffusion model), which can design and optimize drug candidate molecules tailored to a protein’s structure alone—without needing prior information about binding molecules. The model also predicts the binding mechanism (non-covalent interactions) between the drug and the target protein.

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