Health Care Technology

Engineers create ultra-high resolution images in anti-counterfeiting breakthrough


The team of engineers developed nanoscale plasmonic colour filters that display different colours depending on the orientation of the light that hits it. Plasmonic colour filtering has provided a range of new techniques for "printing" images at resolutions beyond the diffraction-limit, significantly improving upon what can be achieved using the traditional, dye-based filtering methods. While a typical printed image in a magazine might consist of around 300 coloured dots per inch of page, or 300 DPI, a page printed with structural colour techniques could reach a resolution of 100,000 DPI or more, the engineers explained. Lead researcher and PhD student at the ANU Research School of Physics and Engineering Lei Wang touted the complex holographic image advancement as opening the door to imaging technologies such as those seen in science fiction movies.

The Key to Reducing Doctors' Misdiagnoses


"Knowledge from systematically analyzing missed opportunities in correct or timely diagnosis will inform improvements and create a learning health system for diagnosis," Dr. Singh says. The network, known as Pride, short for Primary Care Research in Diagnostic Errors, plans to identify, analyze and classify diagnostic errors and delays with the help of electronic medical records, to develop and share interventions that can overcome diagnostic errors and delays, especially in primary care. It also plans to help doctors avoid ordering unnecessary and wasteful tests by developing "principles of conservative diagnosis," says Gordon Schiff, associate director of Brigham and Women's division of general internal medicine and quality and safety director at Harvard Medical School's Center for Primary Care. In response, the project plans to develop and test "loop-closing" tools for electronically tracking doctors' recommendations of tests and procedures that aren't carried out.

Artificial skin lets robot hand feel hot or cold


A robot hand with artificial skin reaches for a glass of ice water. Researchers at the University of Houston have created an artificial skin that allows a robotic hand to sense the difference between heat and cold. The discovery of stretchable electronics could have a significant impact in the wearables market, with devices such as health monitors or biomedical devices, says Cunjiang Yu, an assistant professor of mechanical engineering at the University of Houston and the lead author for the paper. When the stretchable electronic skin was applied to a robotic hand, it could tell the difference between hot and cold water.

Researchers Create Artificial Skin To Give Robots Sense Of Touch

International Business Times

Cunjiang Yu, an assistant professor at the university and three other researchers created "a semiconductor in a rubber composite format" that can stretch and still retain functionality, allowing a robotic hand to feel temperature differences and distinguish between hot and cold. Other than demonstrating the temperature sensitivity of the material using a robotic hand and hot and cold water, the researchers also showed the artificial skin could interpret computer signals and reproduce them in sign language. Researchers from the University of Houston have reported a breakthrough in stretchable electronics that can serve as an artificial skin, allowing a robotic hand to sense the difference between hot and cold. In the open-access paper titled "Rubbery electronics and sensors from intrinsically stretchable elastomeric composites of semiconductors and conductors," the researchers wrote: "Rubbery sensors, which include strain, pressure, and temperature sensors, show reliable sensing capabilities and are exploited as smart skins that enable gesture translation for sign language alphabet and haptic sensing for robotics to illustrate one of the applications of the sensors."

Should The Use of Machine Learning in Healthcare Be Embraced or Met With Skepticism?


Natural language processing (NLP) of unstructured free-text content, such as that found in electronic health record (EHR) clinical notes, used to diagnose disease. Using data provided by the patient upon admission to the hospital and prior to the collection of vital signs, laboratory results or patient history to classify that patient's risk for a particular disease state. Natural language processing (NLP) of unstructured free-text content, such as that found in electronic health record (EHR) clinical notes, used to diagnose disease. Using data provided by the patient upon admission to the hospital and prior to the collection of vital signs, laboratory results or patient history to classify that patient's risk for a particular disease state.



Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. This work is published and licensed by Dove Medical Press Limited. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

Robotics Automation Journals Peer Reviewed


The journal provides an Open Access platform to publish the latest contributions in the field of robotics, automation technologies, robotic surgery, intelligent robotics, mechatronics, and biomimetics novel and biologically-inspired robotics, modelling, identification and control of robotic systems, biomedical, rehabilitation and surgical robotics, exoskeletons, prosthetics and artificial organs, AI, neural networks and fuzzy logic in robotics etc. Editorial board members of the Robotics & Automation or outside experts review manuscripts; at least two independent reviewer's approval followed by the editor is required for the acceptance of any citable manuscript. Editorial Manager System is an online manuscript submission, review and tracking systems. Review processing is performed by the editorial board members of Advances in Robotics & Automation or outside experts; at least two independent reviewers approval followed by editor approval is required for acceptance of any citable manuscript.

Elon Musk could be about to spend $100m linking human brains to computers


A company set up by Elon Musk to develop advanced biotechnology enhancements for the human brain has raised $27m (£20.9m) Neuralink could be seeking as much as $100m within the next 12 months, the filing appears to state, but Mr Musk has taken to Twitter to deny that the company was actively fundraising. And although Mr Musk has been adamant that Neuralink is not looking for further funding, Bloomberg has reported that he "has taken steps to sell as much as $100 million in stock to fund the development". The company's website states it is "developing ultra high bandwidth brain-machine interfaces to connect humans and computers". The biotechnology company, based in San Francisco, is also putting out the call for "exceptional engineers and scientists".

Robots and AI Will Take Over These 3 Medical Niches First


Many training programs are starting to include robotic and virtual reality scenarios to provide hands-on training for students without putting patients at risk. With all of these advances in medical robotics, three niches stand out above the rest: surgery, medical imaging, and drug discovery. By allowing an AI or basic machine learning program to study the medical images, researchers can find patterns and make more accurate diagnoses than ever before. Artificial intelligence, machine learning and predictive algorithms could help speed up this system.

Artificial intelligence: Evolving the practice of medicine from an art to a science


With artificial intelligence (AI)- based tools, doctors will have powerful tools to make better diagnoses and treatment decisions based upon analysis of real-world clinical data and use of strong science. In fact, the research firm, IDC, predicts that global spending on AI and cognitive computing technologies will increase from $12.5 billion this year to $46 billion by 2020. Google is developing a machine learning system trained on one million eye scans to recognize sight-threatening conditions from a simple digital retinal scan. With AI assistance, doctors can concentrate providing their patients guidance, support, and a healing touch, with a focus on "well care," and spend less time on computer clerical work and treatment guess work.