AI Tool could Reduce Common Drug Side Effects

Research led by the University of Exeter and Kent and Medway NHS and Social Care Partnership Trust, published in Age and Ageing, assessed a new tool designed to calculate which medicines are more likely to experience adverse anticholinergic effects on the body and brain. These complications can occur from many -prescription and over-the-counter drugs which affects the brain by blocking a key neurotransmitter called acetylcholine.

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ANregiomed Puts Siemens Healthineers and Dräger in Charge of its Medical Equipment

This new technology partnership kicked off September 1, 2022, marks the first time that Siemens Healthineers is collaborating with a municipal medical service provider to implement a concept it has successfully pursued internationally with major networked healthcare providers and full-service hospitals.

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(Senior) Project Lead Digital Transformation Consulting

Do you want to help create the future of healthcare? Our name, Siemens Healthineers, was selected to honor our people who dedicate their energy and passion to this cause. It reflects their pioneering spirit combined with our long history of engineering in the ever-evolving healthcare industry.

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Consortium to Develop Fully Implantable Brain-Computer Interface to Enable Communication for People with Paralysis

A research consortium led by the UMC Utrecht Brain Center (the Netherlands) in collaboration with Graz University of Technology (Austria), the Wyss Center for Bio and Neuroengineering (Switzerland) and CorTec (Germany) is to receive a grant through the European Innovation Council (EIC) Pathfinder Challenge mechanism.

The researchers aim to develop a unique fully implantable Brain-Computer Interface (BCI) system for people with locked-in syndrome (LIS) - a condition in which paralysis severely limits communication.

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AI Model Outperforms Clinicians in Diagnosing Pediatric Ear Infections

An artificial-intelligence (AI) model built at Mass Eye and Ear was shown to be significantly more accurate than doctors at diagnosing pediatric ear infections in the first head-to-head evaluation of its kind, a research team working to develop the model for clinical use reported.

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NHS Lanarkshire Launches Digital pre-Operative App to Transform Patient Care

NHS Lanarkshire has launched an electronic pre-operative assessment platform to support the delivery of next generation care pathways for its patients.

Working with Buddy Healthcare, the hospital Board has launched "ELSIE", its new digital platform. It's been designed to replace the previous "paper based" system, a process that often required every patient to physically attend a hospital and answer questions with a nurse.

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AI Improves Treatment in Women with Heart Attacks

Heart attacks are one of the leading causes of death worldwide, and women who suffer a heart attack have a higher mortality rate than men. This has been a matter of concern to cardiologists for decades and has led to controversy in the medical field about the causes and effects of possible gaps in treatment.

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AI Model can Detect Parkinson's from Breathing Patterns

Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic

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Roche Launches a Digital PCR System

Roche (SIX: RO, ROG; OTCQX: RHHBY) announced the launch of the Digital LightCyclerⓇ System, Roche's first digital polymerase chain reaction ( PCR) system. This next-generation system detects disease and is designed to accurately quantify trace amounts of specific DNA and RNA targets not typically detectable by conventional PCR methods.

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AI + ECG Heart Trace can Accurately Predict Diabetes and pre-Diabetes

An artificial intelligence (AI) algorithm, derived from the features of individual heartbeats recorded on an ECG (electrocardiogram), can accurately predict diabetes and pre-diabetes, suggests preliminary research published in the online journal BMJ Innovations.

If validated in larger studies, the approach could be used to screen for the disease in low resource settings, say the researchers.

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