Posted in biotech/medical, chemistry, health, robotics/AI
Laboratory “copilots” and automated labs are AI’s latest contribution to speeding up the development of new drugs, chemicals and materials. Why it matters: Scientific discovery itself must speed up if the world is to address its challenges — from climate change to personalized treatments for cancer — fast enough to make a difference. In scientific research, “manual effort is not scalable,” writes Microsoft Health Futures’ Hoifung Poon in the…
Nabla Copilot sits as a web app or Chrome extension on a doctor’s computer and listens to their consultation with patients.
Nabla Copilot generates clinical notes almost instantly, saving doctors the trouble of documenting the information manually.
An AI has created stills from a non-existent Alejandro Jodorowsky version of “The Matrix” that “stars” Jeff Goldblum as Morpheus — and it’s honestly epic.
The Wall Street bank estimates that specialized memory and storage products meant for AI chips will help chipmakers regain pricing power and raise earnings.
YSM researchers are using deeplearning AI models to improve detection of patients at risk for multiple hospitalizations due to asthma and COPD.
Asthma and chronic obstructive pulmonary disease (COPD) are two of the most common lung diseases worldwide, and exacerbation of these conditions can negatively impact health and increase health care costs. A new study shows that deep learning, a type of artificial intelligence (AI) that uses large amounts of data to process information, can improve detection of patients with these diseases who are at increased risk for multiple hospitalizations.
The study was published Dec. 13, 2023, in the journal Respiratory Research.
In the study, researchers identified electronic health record (EHR) characteristics of severe asthma and COPD exacerbations. They then evaluated four machine learning models and one deep learning model in predicting hospital readmissions using EHR data. The researchers found that multilayer perceptron, a deep learning method, had the best performance.
Powered by Blue Origin engines, United Launch Alliance’s Vulcan rocket launched Astrobotic’s moon lander on a lunar odyssey.
German researchers are developing an algorithm to help decode ancient cuneiform tablets — including those containing the oldest known work of world literature.
Ancient poem: The Epic of Gilgamesh is a Babylonian poem first written in cuneiform characters on clay tablets around 4,000 years ago. It tells the story of Gilgamesh, the king of the city of Uruk, and his quest for immortality.
Over the centuries, the poem was copied onto countless other tablets in both the original Sumerian language as well as Akkadian.
About 75,000 staff to be given access to ‘PairD’ tool with advice to validate ‘accuracy and completeness’
Our smart devices take voice commands from us, check our heartbeats, track our sleep, translate text, send us reminders, capture photos and movies, and let us talk to family and friends continents away.
Now imagine turbocharging those capabilities. Holding in-depth, natural language exchanges on academic or personal queries; running our vital signs through a global database to check on imminent health issues; packing massive databases to provide comprehensive real-time translation among two or more parties speaking different languages; and conversing with GPS software providing details on the best burgers, movies, hotels or people-watching spots trending along your route.
Tapping into the seductive power of large language models and natural language processing, we’ve witnessed tremendous progress in communications between us and technology that we increasingly rely on in our daily lives.
The integration of mechanical memory in the form of springs has for hundreds of years proven to be a key enabling technology for mechanical devices (such as clocks), achieving advanced functionality through complex autonomous movements. Currently, the integration of springs in silicon-based microtechnology has opened the world of planar mass-producible mechatronic devices from which we all benefit, via air-bag sensors for example.
For a new generation of minimally and even non-invasive biomedical applications however, mobile devices that can safely interact mechanically with cells must be achieved at much smaller scales (10 microns) and with much softer forces (pico Newton scale, i.e., lifting weights less than one millionth of a mg) and in customized three-dimensional shapes.
Researchers at the Chemnitz University of Technology, the Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences and the Leibniz IFW Dresden, in a recent publication in Nature Nanotechnology, have demonstrated that controllable springs can be integrated at arbitrary chosen locations within soft three-dimensional structures using confocal photolithographic manufacturing (with nanoscale precision) of a novel magnetically active material in the form of a photoresist impregnated with customizable densities of magnetic nanoparticles.
