Also, the Hong Kong Polytechnic University has opened a new laboratory with Diagens Tech for developing medical AI applications.
Lookout CTO David Richardson says mobile devices need to be treated like computers rather than phones because behind-the-scenes artificial intelligence running unsanctioned or misconfigured third-party apps can introduce data leakage.
America.gov, powered by Gemini and Grok LLMs, is meant to modernize federal operations, bringing convenience and the ability to complete transactions, such as Medicare enrollment, through a single chatbot interface.
Fragmented records and imprecise coding can undermine valuable analytics initiatives, says IMO Health's Joseph Zabinski, who explains what health systems should demand before scaling them.
MIT engineers have found a way to stabilize the lipid nanoparticles used to deliver RNA vaccines, which could allow the vaccines to be more widely distributed.
RNA vaccines, which have been proven effective against Covid-19, are now being developed for many other diseases, including cancer. One of the drawbacks to these vaccines is that they require ultracold storage, but researchers from MIT have found a promising way to overcome that limitation.
With help from an AI algorithm, the researchers tweaked the formulation surrounding the lipid nanoparticles that are typically used to deliver mRNA vaccines, making the vaccines more heat-resistant. Using this approach, they formulated vaccines that could remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months.
When Covid-19 vaccines carried by these particles were administered to mice, they generated just as strong an immune response as an RNA Covid-19 vaccine similar to one developed by Moderna. By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.
Researchers from the COR group at the Institute of Information and Communications Technologies (ITACA) of the Universitat Politècnica de València (UPV) have developed an artificial intelligence model capable of localizing and quantifying tissue abnormalities associated with atrial cardiomyopathy using electrical recordings from the body's surface.
The system, based on graph neural networks (Graph Neural Networks, GNNs), achieved an accuracy of 89% in localising the affected areas and 84% in determining the extent of damage to atrial tissue. Furthermore, it maintained this capability when analysing anatomical structures not used during its training.
Researchers at Baylor College of Medicine, the Duncan Neurological Research Institute (Duncan NRI) at Texas Children's Hospital, the Texome Project and collaborating institutions have identified variants in gene BRSK1 as a likely diagnosis for individuals with a rare and complex neurodevelopmental disorder who until now had not received an explanation for their condition. The study appears in the American Journal of Human Genetics.
Why the biggest gains will be incremental, how AI can surface overlooked insights, and why success must be measured in care quality and time returned to clinicians.
Aging progressively affects the functioning of our body and, among other things, deteriorates the ability of the hematopoietic system- the set of organs and tissues responsible for producing blood cells- to maintain adequate blood cell production. Understanding and measuring this process is especially relevant to study how blood stem cells age and to identify strategies to preserve or recover their function.
The study, led by Dr Maria Carolina Florian, a researcher at the program of Regenerative Medicine at Bellvitge Biomedical Research Institute (IDIBELL) and ICREA Research Professor, and Dr Paula Petrone, a researcher at the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS), and the Barcelona Institute for Global Health (ISGlobal), a centre supported by the "la Caixa" Foundation, presents ChromAgeNet, an artificial intelligence-based tool that identifies aging-associated patterns in microscopy images of hematopoietic stem cells by analyzing the 3D organization of chromatin, the material made up mainly of DNA and proteins found in the cell nucleus that packages DNA and regulates which genes are active, ultimately determining cell identity and function. The work has been a central part of the doctoral thesis of Pablo Iañez, researcher at ISGlobal, and brings together expertise in stem cell biology, aging, image analysis and artificial intelligence. The results are published in Aging Cell, an international journal of reference in the field of aging research.
To develop the model, the researchers analyzed three-dimensional images of mouse hematopoietic stem cell nuclei, stained with DAPI, a simple and widely used technique for visualizing DNA. Using a convolutional neural network -a type of artificial intelligence model designed to analyze images-, ChromAgeNet learned to distinguish young cells from aged cells.
Based on the appearance of the nucleus, the model demonstrated a 77% probability of correctly distinguishing cells into two groups, outperforming even a machine learning model based on chromatin features previously defined by the researchers.
With highly defined prompts that surface relevant context, artificial intelligence can identify patients at risk of day-of-surgery cancellations, says one healthcare leader who created such an agent at the second Epic Agent Factory Build-a-Thon.
In a recent selective review published in the journal Signal Transduction and Targeted Therapy, the authors examined how artificial intelligence (AI) can support biomarker discovery, validation, biological interpretation, clinical utility, and therapeutic integration across diseases.
Cardiologist Dr. Joshua Cohen explains how advanced plaque analysis is changing risk assessment, workflow and implementation priorities in cardiac imaging at the Virginia health system.
