New York: Researchers, including one of an Indian-origin, have developed a wearable off-the-shelf and machine learning technology that can predict an individual's blood pressure and provide personalised recommendations to lower it. When doctors tell their patients to make a lot of significant lifestyle changes - exercise more, sleep better, lower their salt intake etc. - it can be overwhelming, and compliance is not very high, Sujit Dey, Professor, Department of Electrical and Computer Engineering at the University of California in the US, said in a statement. "What if we could pinpoint the one health behaviour that most impacts an individual's blood pressure, and have them focus on that one goal instead," Dey said. The study affirmed the importance of personalised data over generalised information as the former was more effective. The team collected sleep, exercise and blood pressure data from eight patients over 90 days.
A recent market report by Grand View Research, the deep learning market size is expected to touch $10.2 billion by 2025. What is fueling this growth is chip advancement and the increasing GPU-accelerated applications that have led to the widespread adoption of open-sourced DL frameworks. Another key area is that organisations are realising the need to extract valuable insights from data and develop better customer-centric products. Another research firm, Stratistics MRC, indicated that DL has exponential growth opportunity and the technology will be heavily utilised in mobile devices and healthcare sector, specifically for medical image analysis. In fact, deep learning technology will also play a pivotal role in the manufacturing industry with the DL being leveraged for powering machine vision systems, industrial robots and improve production cycle.
Artificial intelligence (AI) is one of the prime technologies leading the wave of disruption that is going on within the health care sector. Recent studies have shown that AI technology can outperform doctors when it comes to cancer screenings and disease diagnoses. In particular, this could mean specialists such as radiologists and pathologists could be replaced by AI technology. Per an article by the Association of American Medical Colleges, "a New England Journal of Medicine article predicted that'machine learning will displace much of the work of radiologists and anatomical pathologists,' adding that'it will soon exceed human accuracy.' That same year, Geoffrey Hinton, PhD, a professor emeritus at the University of Toronto who also designs machine learning algorithms for Google (and who received the Association for Computing Machinery's A.M. Turing Award, often called the Nobel Prize of computing, in 2019), declared, 'We should stop training radiologists now.'"
PROPONENTS of education technology have made remarkable promises over the past two decades: that by 2019, half of all secondary school courses would be online; videos and practice problems can let students learn mathematics at their own pace; in 50 years only 10 mega-institutions of higher education would be left; or that typical students left alone with internet-connected computers can learn anything without the help of schools or teachers. Then in 2020, people around the world were forced to turn to online learning as the coronavirus pandemic shut down schools serving more than 1 billion students. It was education technology's big moment, but for many students and families, remote learning has been a disappointment. When the world needs it most, why has education technology seemed so lacklustre? Educational software has a long history, but throughout there have been two major challenges.
Researchers believe that the industry will contribute more than $5.6 trillion to the economy by 2025 as well. Much of this revenue comes from the medical research field, which is responsible for improving drug research, disease diagnosis and treatment protocols. Major research companies are collaborating with software development services to integrate deep learning technology into their investigations. Deep learning promises to transform the way that doctors review medical tests and make diagnoses, helping them identify diseases and start treatment quicker. The technology will also help pharmaceutical companies develop life-saving drugs in a shorter amount of time.