The dataset of scans is from more than 30,000 patients, including many with advanced lung disease. The NIH Clinical Center recently released over 100,000 anonymized chest x-ray images and their corresponding data to the scientific community. The release will allow researchers across the country and around the world to freely access the datasets and increase their ability to teach computers how to detect and diagnose disease. Ultimately, this artificial intelligence mechanism can lead to clinicians making better diagnostic decisions for patients. NIH compiled the dataset of scans from more than 30,000 patients, including many with advanced lung disease.
Pure Storage (NYSE: PSTG), a leading independent all-flash data platform vendor for the cloud era, announced significant customer momentum for FlashBlade, the system purpose-built for modern analytics. Since general availability in January 2017, FlashBlade has gained traction among organizations running and innovating with emerging workloads, specifically modern analytics, artificial intelligence (AI) and machine learning (ML). Data is at the center of the modern analytics revolution. Large amounts of data must be delivered to the parallel processors, like multi-core CPUs and GPUs, at incredibly high speeds in order to train machine learning and analytic algorithms faster and more accurately. Today, most machine learning production is undertaken by hyperscalers and large, web-scale companies.
DARPA (Defense Advanced Research Projects Agency), is a division of the American Defense Department that investigates new technologies. It has for some time regarded the current generation of AI technologies as important in the future. It has been in the forefront of AI research in image recognition, speech recognition and generation, robotics, autonomous vehicles, medical diagnostic systems, and more. However, DARPA is well aware that despite the high level of problem-solving capabilities of AI programs – they lack explainability. AI deep learning algorithms use complex mathematics that is very difficult for human users to understand or comprehend.
In the latest of our Predictions series we take a look at what the future of healthcare will look like. Could artificial intelligence and smart technology improve every stage of our lives? We look at how the future of healthcare will affect us from birth including wearable tech and the internet of things to capturing baseline health data we can use to monitor our health as we grow older. From womb to tomb Health scanning and data will become ever present in our lives – even from the very start of life. Before birth, scanning will take place in the womb which will create a basic profile of a person's health and create treatment plans from the very start.
IBM scientists Thomas Brunschwiler and Rahel Straessle are developing machine learning algorithms to interpret the IoT data. COPD, is a progressive lung disease which causes breathlessness and is often caused by cigarette smoke and air pollution. By 2030, it is expected to be the third leading cause of death worldwide, with 90% occurring in low and middle-income countries, according to the World Health Organization. The Centers for Disease Control and Prevention reports that by 2020 the expected cost of medical care for adults in the US with COPD will be more than $90 billion, mainly due to complications and multiple hospitalizations, many of which are preventable with better healthcare management and more personalized and frequent patient support. Management and prevention of COPD is the focus of a new research project presented today at the 19th annual IEEE Healthcom Conference, in Dalian, China.
Machine learning promises to dramatically improve the efficiency and effectiveness of healthcare, bringing us closer to the kind of personalized medicine that not only can substantially improve preventive care, but also bring the right treatment to the right individuals at the right time. We're seeing growing application in medical imaging analysis, along with tools that use artificial intelligence to improve medication adherence and follow-up care. However, when it comes to predicting, diagnosing and treating medical conditions, many are still skeptical. As with any analytics solution, the quality of the results is only as good as the quality of the data the system has to work with. Small sample sizes, "dirty" or incomplete data and biased data can all impact the analysis, which could result in skewed conclusions.
She consults an app on her phone, which asks an increasingly sophisticated series of diagnostic questions. The app also takes in data from Janet's fitness trackers that monitor heart rate, blood pressure and blood sugar. The app decides that Janet's symptoms look serious, and it arranges a video chat with a human doctor to discuss options so that potentially bad news can be presented in a more "human" way. The doctor has access to Janet's data remotely, along with access to a more sophisticated diagnostic, Artificial Intelligence. During that consultation, Janet is booked into a clinic for medical imaging scans to aid in further diagnosis.
We work under the threat of being replaced by machines smarter than us. Vinod Khosla, a Silicon Valley venture capitalist, says that the medical profession is approaching extinction and predicts that the majority of our work will eventually be outsourced to algorithms and other artificial tools of clinical reasoning. But I think my profession is headed to evolution, not extinction. Much of what we once did with our eyes, hands, and ears has been replaced by machines. In my corner of the United States, a child who comes to an emergency department with abdominal pain is likely to have a CT scan before ever being examined by a physician.
Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they're still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. One common cause of false positives are so-called "high-risk" lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time.
We are not used to the idea of machines making ethical decisions, but the day when they will routinely do this - by themselves - is fast approaching. So how, asks the BBC's David Edmonds, will we teach them to do the right thing? The car arrives at your home bang on schedule at 8am to take you to work. You climb into the back seat and remove your electronic reading device from your briefcase to scan the news. There has never been trouble on the journey before: there's usually little congestion.