HD Medical touts the world’s first intelligent stethoscope and ECG that doesn’t need long wires and can monitor heart health remotely.
Join CNET during CES 2021 for talks with three medical luminaries to discuss what we’ve gained — and need to fix — with telehealth over a turbulent pandemic year.
OraSure Technologies has blazed a trail in at-home diagnostic tests. Now, the Pennsylvania-based biotech company is working to produce a quick, over-the-counter coronavirus test that consumers can take in the privacy of their home with results available in minutes. NPR’s Allison Aubrey reports.
It has taken time — some say far too long — but medicine stands on the brink of an AI revolution. In a recent article in the New England Journal of Medicine, Isaac Kohane, head of Harvard Medical School’s Department of Biomedical Informatics, and his co-authors say that AI will indeed make it possible to bring all medical knowledge to bear in service of any case.
Properly designed AI also has the potential to make our health care system more efficient and less expensive, ease the paperwork burden that has more and more doctors considering new careers, fill the gaping holes in access to quality care in the world’s poorest places, and, among many other things, serve as an unblinking watchdog on the lookout for the medical errors that kill an estimated 200,000 people and cost $1.9 billion annually.
“I’m convinced that the implementation of AI in medicine will be one of the things that change the way care is delivered going forward,” said David Bates, chief of internal medicine at Harvard-affiliated Brigham and Women’s Hospital, professor of medicine at Harvard Medical School and of health policy and management at the Harvard T.H. Chan School of Public Health. “It’s clear that clinicians don’t make as good decisions as they could. If they had support to make better decisions, they could do a better job.”
NYU Langone’s Kimmel Pavilion is home to the region’s newest and most technologically sophisticated neurosurgery suite. Designed to optimize patient care, our facilities are just one reason U.S. News & World Report’s “Best Hospitals” ranks NYU Langone among the top 10 hospitals in the country for neurology and neurosurgery.
Learn more about neurosurgery at NYU Langone and meet our renowned surgeons: https://nyulangone.org/locations/neur…
In the future, remote monitoring of health data using wireless–enabled devices that measure a person’s weight, blood pressure, blood sugar, pulse, and heart rhythm could further advance telehealth’s promise.
“I imagine a world where we are continuously monitoring key health factors and using artificial intelligence to monitor those signals,” says Dr. Schwamm.
From a patient’s perspective, virtual visits save a lot of time. You don’t need to take time off work or other commitments to drive, park, and sit in a waiting room before your visit. And even though you’re not in the same room, you may actually get more direct eye contact with your physician, thanks to the face-to-face nature of video calling.
Another advantage: you may be able to have another person — such as a family member who lives across town or across the country — join the video call. That could be especially helpful if you’re facing an upcoming procedure or discussing a serious health concern. Just as with in-person visits, it’s nice to have another person listening, asking questions, and taking notes.
This training guide provides instructions on how to install Keck Medicine of USC’s TeleCARE platform on your Android phone.
Overall, the algorithm correctly identified the presence of diabetes in up to 81 percent of patients in two separate datasets. When the algorithm was tested in an additional dataset of patients enrolled from in-person clinics, it correctly identified 82 percent of patients with diabetes.
In the Nature Medicine study, UCSF researchers obtained nearly 3 million PPG recordings from 53,870 patients in the Health eHeart Study who used the Azumio Instant Heart Rate app on the iPhone and reported having been diagnosed with diabetes by a health care provider. This data was used to both develop and validate a deep-learning algorithm to detect the presence of diabetes using smartphone-measured PPG signals.
Among the patients that the algorithm predicted did not have diabetes, 92 to 97 percent indeed did not have the disease across the validation datasets. When this PPG-derived prediction was combined with other easily obtainable patient information, such as age, gender, body mass index and race/ethnicity, predictive performance improved further.