May 14, 2020
Artificial intelligence is knocking on the healthcare industry's doors. From computation-driven R&D for new drugs, to objective image analysis, to mass-scale disease screening and diagnosis, more and more instances of the technology are pitching solutions to some of the industry's most pressing challenges.
Regulators once stood as the primary hurdle for AI and associated technologies like...
April 27, 2020
Implementation of either an automated or semi-automated deep learning system for diabetic retinopathy screening could lead to cost savings at the health-system level, according to an economic analysis modeling study recently published in The Lancet Digital Health.
Backed by Singapore's Ministry of Health, the investigation looked at data from a national diabetic retinopathy screening program...
Credit: Weldmar Connect, Inhealthcare
April 9, 2020
EVERGREEN LIFE BUILDS HEAT MAP SHOWING INCIDENCES OF COVID-19 IN UK
Healthcare app Evergreen Life is compiling a heat map that exposes trends in local behaviour during the coronavirus pandemic. The map outlines whether users are experiencing symptoms of COVID-19 and if they are abiding by government advice to stay at home (excluding key workers). So far, it has collected more than 50,000...
October 15, 2019
French health tech startup Primaa has raised €2m to accelerate the development and the international marketing of its automated diagnostic products.
The Paris-based firm, founded in 2018, develops software tools which use artificial intelligence (AI) and deep learning to assist anatomical pathologists with cancer and other diagnoses.
It is currently in the process of finalising its first tool...
May 9, 2018
Google has unleashed a tidal wave of product and feature updates through the ongoing Google I/O developers’ conference, and it’s no surprise that the intersection of artificial intelligence and healthcare was a recurring spotlight among them. Through keynote speeches and simultaneously released online blog posts, the company highlighted a handful of tech-driven healthcare efforts that seem to be...
May 11, 2017
Cardiogram, a startup working on algorithms to make the Apple Watch’s heart rate data clinically actionable, announced some results today from its mRhythm Study. The data, presented at the Heart Rhythm Society’s 38th Annual Scientific Sessions, shows that the company’s algorithms can detect atrial fibrillation with 97 percent accuracy.
“Our results show that common wearable trackers like...
April 26, 2017
While medical imaging technologies have gotten more sophisticated and commonplace over the years, the number of available radiologists or clinicians to read things like CT scans or MRIs haven’t increased accordingly. But tools that leverage machine learning have gotten smarter, and engineers believe they could play a vital role in alleviating the high demands on human medical image interpreters....
April 24, 2017
Out of the 422 million people around the world living with diabetes, one in three of them will develop diabetic retinopathy (DR), a common condition that can lead to permanent blindness if left untreated. While early detection and treatment can dramatically reduce that risk, a third of people with diabetes have never even been screened for DR, as many are living in low-income, medically...
March 16, 2017
Intermountain Healthcare is working with Zebra Medical Vision, a deep learning imaging analytics company, based in Israel to integrate machine learning in medical imaging analysis. The goal is to provide better patient care.
Here’s how it works: Zebra-Med’s analytics engine receives imaging data and analyzes findings indicative of cardiovascular, pulmonary, metabolic and bone health....
November 29, 2016
A team of Google researchers has published a paper in the Journal of the American Medical Association showing that Google's deep learning algorithm, trained on a large data set of fundus images, can detect diabetic retinopathy with better than 90 percent accuracy.
"These results demonstrate that deep neural networks can be trained, using large data sets and without having to specify lesion-based...