NEW YORK: The outcome of robotic-assisted surgery and traditional open surgery are equally effective in treating bladder cancer, say researchers, led by one of an Indian-origin. The results, published in the journal The Lancet, may help patients and doctors to make informed decisions on the use of robotic surgery, which is not cheap, the researchers said. There has been an assumption that patients who receive robotic surgery will perceive a better quality of life than patients who have open surgery. However, the trial showed that both groups had a significant return to their previous quality of life, and there was no advantage of one group over the other at three and six months after surgery. "We have done more than four million surgeries with the robotic approach since the device came into existence, and on average we do close to a million robotic surgeries a year globally," said Dipen J. Parekh, Chief Clinical Officer at the University of Miami, Florida in the US.
Using raw data from the entirety of a patient's electronic health record, Google researchers have developed an artificial intelligence network capable of predicting the course of their disease and risk of death during a hospital stay, with much more accuracy than previous methods. The deep learning models were trained on over 216,000 deidentified EHRs from more than 114,000 adult patients, who had been hospitalized for at least one day at either the University of California, San Francisco or the University of Chicago. For those two academic medical centers, the AI predicted the risks of mortality, readmission and prolonged stays, as well as discharge diagnoses, by ICD-9 code. The network was 95% accurate in predicting a patient's risk of dying while in the hospital--with a much lower rate of false alerts--than the traditional regressive model--the augmented Early Warning Score--which measures 28 factors and was about 85% accurate at the two centers. The researchers' findings were published last month in the Nature journal npj Digital Medicine.
With the enough data, the company thinks it can predict when a patient will die with up to 95 per cent accuracy. In May, Google scientists published the account of a woman who came to hospital with late stage breast cancer and fluid building in her lungs. After the hospital equipment and computers took the woman's vital signs, it estimated that she had a 9.3 per cent chance of dying during her stay at the hospital. Then it was Google's turn. Its neural network, a type of artificial intelligence that can analyse huge reams of data and automatically learn and improve, was fed 175,639 data points on the woman including past health records and her current vital signs.
A woman with late-stage breast cancer came to a city hospital, fluids already flooding her lungs. She saw two doctors and got a radiology scan. An new type of algorithm created by the company read up on the woman -- 175,639 data points -- and rendered its assessment of her death risk: 19.9 percent. She passed away in a matter of days. The harrowing account of the unidentified woman's death was published by Google in May in research highlighting the health-care potential of neural networks, a form of artificial intelligence software that's particularly good at using data to automatically learn and improve.
When you think of artificial intelligence (AI), you might not immediately think of the healthcare sector. However, that would be a mistake. AI has the potential to do everything from predicting readmissions, cutting human error and managing epidemics to assisting surgeons to carry out complex operations. Here we take a closer look at three intriguing stocks using AI to forge new advances in treating and tackling disease. To pinpoint these three stocks, we used TipRanks' data to scan for'Strong Buy' stocks in the healthcare sector.
Artificial intelligence can do some remarkable things, from driving cars to improving breast cancer diagnosis. And now it can make memes. Abel Peirson and Meltem Tolunay, two researchers at Stanford University, recently posted a paper to the arXiv preprint server detailing a new machine learning model they created that's capable of creating pretty convincing memes: Peirson and Tolunay trained their machine learning algorithm with a dataset of more than 400 types of memes with multiple captions that they pulled from memegenerator.com using a Python script. To simplify the task, they focused only on "advice animal" style memes, the kind where an image of a specific character like "socially awkward penguin" is overlaid with a caption of text that represents the traits of that character, usually with a humorous observation. "This allows for relatively simple collection of datasets," the researchers wrote.
The use of Artificial Intelligence (AI) in healthcare has the promise to reach a new era with companies like Imagia leading the way. Founded in 2015, the AI healthcare company's purpose is to improve outcomes for patients, with a specific focus on oncology. Imagia does this by leveraging information from routine medical imaging, reports and other data. Although there is no doubt of its potential, we have yet to see the proper utilization of AI in health care. There are many challenges to developing and implementing this technology into routine care, including access to good quality data, staying clinically relevant, and getting solutions directly to patients and physicians.
These patterns are wirelessly sent to rooms full of whirring, blinking supercomputers that translate them into words, meanings and actions. Behind this technology are decades of artificial intelligence research and millions of lines of computer code. We stand on the shoulders of giants when we say, Play Beethoven's Fifth, and our device responds with music to our ears: "da-da-da DUM," the opening of the composer's most famous symphony. Today, Stanford Medicine researchers are exploring ways to use intelligent listening technologies, natural language processing, machine learning and data mining to deliver better, more efficient health care. Here are a few of these projects.
Researchers utilized five different machine learning approaches to accurately spot lymphedema--a negative side effect of breast cancer treatment--which may help detect it earlier and improve treatment. The study was published in the May edition of the journal mHealth. "Using a well-trained classification algorithm to detect lymphedema based on real-time symptom reports is a highly promising tool that may improve lymphedema outcomes," said lead author Mei R Fu, PhD, and associate professor of nursing at New York University in an NYU release. A web-based tool collected information from 355 women who had undergone treatment for breast cancer, including surgery. Participants shared demographic data and clinical information and were asked if they were experiencing any 26 different lymphedema-related symptoms.
Skin cancer was found to be diagnosed more accurately by artificial intelligence than experienced dermatologists in a new international study. Researchers tested a form of machine learning known as a deep learning convolutional neural network (CNN) to reach this conclusion. The study titled "Artificial intelligence for melanoma diagnosis: How can we deliver on the promise?" was published in the cancer journal Annals of Oncology on May 28. Malignant melanoma accounts for 1 percent of all skin cancers but causes a majority of skin cancer-related deaths. The American Cancer Society estimates 9,320 people will die from melanoma in 2018 while 91,270 new cases will be diagnosed.