A few years back I jumped on the "machine learning will eliminate the need for radiologists" bandwagon. In my failure, however, I'm joined by the biggest experts in deep learning, like Geoffrey Hinton, who in 2016 proclaimed it was "just completely obvious [that] within five years deep learning is going to do better" than trained radiologists. And as an industry, we all keep being wrong about how fast deep learning, a branch of machine learning, will progress. Or not really "progress," because deep learning is progressing, and quickly. What it's not doing, however, is progressing to the point that it's displacing people.

Amsterdam, March 1, 2022 – Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, PhD, Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, USA. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases."

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have transformed many industries and areas of science. Now, these tools are being applied to address the challenges of cancer biomarker discovery, where the analysis of vast amounts of imaging and molecular data is beyond the ability of traditional statistical analyses and tools. In a special issue of Cancer Biomarkers, researchers propose various approaches and explore some of the unique challenges of using AI, DL, and ML to improve the accuracy and predictive power of biomarkers for cancer and other diseases. "The biomarker field is blessed with a plethora of imaging and molecular-based data, and at the same time, plagued with so much data that no one individual can comprehend it all," explained Guest Editor Karin Rodland, Ph.D., Pacific Northwest National Laboratory, Richland; and Oregon Health and Science University, Portland, OR, U.S.. "AI offers a solution to that problem, and it has the potential to uncover novel interactions that more accurately reflect the biology of cancer and other diseases." Promising applications of AI, DL, and ML presented in this issue include identifying early-stage cancers, inferring the site of the specific cancer, aiding in the assignment of appropriate therapeutic options for each patient, characterizing the tumor microenvironment, and predicting the response to immunotherapy.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today's world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works. While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI. The enterprise attack surface is massive.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today's world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works. While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI. The enterprise attack surface is massive.

A computational approach has the potential to help clinicians in intensive care units make better decisions about ordering common blood tests. Results of their study, presented earlier this month at the 2019 Pacific Symposium on Biocomputing, showed that using a machine learning algorithm developed by Princeton University researchers could have reduced the number of lab orders for white blood cell tests by as much as 44 percent. In addition, researchers demonstrated that their approach would have helped inform clinicians to intervene sometimes hours sooner when a patient's condition began to deteriorate. "With the lab test ordering policy that this method developed, we were able to order labs to determine that the patient's health had degraded enough to need treatment, on average, four hours before the clinician actually initiated treatment based on clinician ordered labs," says Barbara Engelhardt, senior author of the study and associate professor of computer science at Princeton. In their study, researchers leveraged the MIMIC III database--which includes detailed medical records of 58,000 critical care admissions at Boston's Beth Israel Deaconess Medical Center--and selected a subset of 6,060 records of adults who were admitted to the ICU between 2001 and 2012.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today's world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works. While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI. The enterprise attack surface is massive.

Google brings video to the talking speaker category with the new Google Home Hub. USA TODAY's Jefferson Graham explains why the device has potential. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Google brings video to the talking speaker category with the new Google Home Hub.

There's a lot of talk about the applicability of artificial intelligence (AI) and deep learning to taming the vast quantities of data that modern Operations teams and their tools deal with. Analyst reports frequently tout AI capabilities, no matter how minor, as a strength of a product, and the lack of them as a weakness. Yet no effective use of AI seems to have emerged and claimed wide adoption in Network Operations or Server Monitoring. Why not? (Disclaimer: LogicMonitor does not currently have deep learning or other AI capabilities). Part of the issue is that AI is a soft definition.

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."