The graph represents a network of 1,103 Twitter users whose recent tweets contained "Thomas Harrer", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Wednesday, 29 April 2020 at 01:43 UTC. The tweets in the network were tweeted over the 7-day, 15-hour, 26-minute period from Tuesday, 21 April 2020 at 10:08 UTC to Wednesday, 29 April 2020 at 01:35 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

The graph represents a network of 973 Twitter users whose recent tweets contained "Thomas_Harrer", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Friday, 17 April 2020 at 22:49 UTC. The tweets in the network were tweeted over the 7-day, 21-hour, 30-minute period from Friday, 10 April 2020 at 00:30 UTC to Friday, 17 April 2020 at 22:01 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.

The graph represents a network of 1,432 Twitter users whose recent tweets contained "Thomas_Harrer", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Tuesday, 04 February 2020 at 21:31 UTC. The tweets in the network were tweeted over the 7-day, 13-hour, 26-minute period from Tuesday, 28 January 2020 at 07:47 UTC to Tuesday, 04 February 2020 at 21:13 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

What if there was a way to give everyone suffering from conditions like paralysis or Locked-in syndrome the means to operate prosthetic devices and tech gadgets using mind-control? Well, there is – or at least, there will be. IBM Research recently developed an end-to-end proof-of-concept for a method of controlling an off-the-shelf robotic arm with a brain-computer interface built using a take-home EEG monitor. To accomplish this, the researchers developed AI to interpret the data from the EEG monitor as commands for the robotic arm. That may not sound like something that will change everything overnight – and IBM isn't the only or first company to dabble in brain-computer interfaces.

You are free to share this article under the Attribution 4.0 International license. Researchers report that they've used a mobile, brain-inspired processor to analyze brain signals from retrospective patient data and successfully predict an average of 69 percent of seizures across all patients with artificial intelligence. The research could help pave the way for personalized seizure prediction for patients with epilepsy. "Our algorithm also allows for instantaneous and easy adjustment, giving patients the flexibility to control how sensitive and in advance the warning is…" With a third of epilepsy patients worldwide currently living with unpredictable seizures that are not adequately controlled through medication or otherwise. This research could dramatically improve the lives of 250,000 Australians and 65 million people worldwide, says Mark Cook, director of the University of Melbourne's Graeme Clark Institute for Biomedical Engineering and director of neurology at St. Vincent's Hospital in Melbourne.

One of the toughest aspects of having epilepsy is not knowing when the next seizure will strike. A wearable warning system that detects pre-seizure brain activity and alerts people of its onset could alleviate some of that stress and make the disorder more manageable. To that end, IBM researchers say they have developed a portable chip that can do the job; they described their invention today in the Lancet's open access journal eBioMedicine. The scientists built the system on a mountain of brainwave data collected from epilepsy patients. The dataset, reported by a separate group in 2013, included over 16 years of continuous electroencephalography (EEG) recordings of brain activity, and thousands of seizures, from patients who had had electrodes surgically implanted in their brains.

In Melbourne, Australia, Stefan Harrer is running an artificial software brain atop an artificial hardware brain in an effort to analyze a brain that isn't artificial at all. Ultimately, he and his colleagues envision merging these three brains together so that the artificial can augment the real. Harrer is an IBM researcher stationed at the company's Australian research lab. Together with neurologists at the University of Melbourne, he's developing a computing system that can analyze your brain waves in an effort to predict epileptic seizures. 'Our aim is to replace broken neural systems with machines.'