Besides, we observe that existing works still cannotdirectlypropagate the gradient information from deep layers to shallow layers.

Policy inference plays an essential role in the contextual bandit problem. In this paper, we use empirical likelihood to develop a Bayesian inference method for the joint analysis of multiple contextual bandit policies in finite sample regimes. The proposed inference method is robust to small sample sizes and is able to provide accurate uncertainty measurements for policy value evaluation. In addition, it allows for flexible inferences on policy comparison with full uncertainty quantification. We demonstrate the effectiveness of the proposed inference method using Monte Carlo simulations and its application to an adolescent body mass index data set.

In addition to responding to all kinds of questions and generating never-before-seen images, artificial intelligence has significant applications for medicine. The magazine Nature published a study in which AI improves on human results in evaluating echocardiogram images, used to diagnose cardiac problems. The authors, a multidisciplinary team at Los Angeles's Cedars-Sinai Medical Center, did a randomized blind study --the first of its kind with this technology-- to evaluate the AI's precision analyzing 3,495 echocardiogram images that show the heart's functioning. In the study, cardiologists were asked to assess evaluations that either technicians or AI software made of the ultrasound images. The doctors corrected mistakes in 16.8% of AI evaluations, compared to 27.2% of human ones. Additionally, the cardiologists could not tell which evaluations were done by AI and which by humans.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Artificial intelligence (AI) could potentially do a better job of screening for heart health than trained sonographers. This is the finding of a study from the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine at Cedars-Sinai in Los Angeles, California. In the study, published in the journal Nature, a total of 3,495 heart echocardiograms (ultrasounds) were assessed.

An AI trained to assess heart scans outperformed human technicians, both in terms of accuracy and efficiency, in a first-of-its-kind trial. "There's a lot of hype and a lot of excitement around AI, but really this is the first piece of very concrete evidence that this is ready for clinical use," said trial leader David Ouyang, a cardiologist at Cedars-Sinai Medical Center. The challenge: Measuring the percentage of available blood that leaves the heart with each pump -- known as the "left ventricular ejection fraction" (LVEF) -- can help doctors assess heart function and determine treatment plans for cardiovascular disease. "There has been much excitement about the use of AI in medicine, but the technologies are rarely assessed in prospective clinical trials." Traditionally, LVEF is determined using echocardiograms, which are ultrasound videos of the heart.

Diagnosing cardiac pathologies from echocardiograms correctly can be an extremely challenging endeavor that only very skilled cardiologists can perform with ease. This latest breakthrough contains the potential to completely shift the narrative when it comes to diagnostic medicine, and can ultimately save countless lives in the near future. Previously, researchers at the Smidt Heart Institute and Stanford University developed one of the first artificial intelligence technologies to assess cardiac function, specifically, left ventricular ejection fraction--the key heart measurement used in diagnosing cardiac function. Their research was published in the prestigious journal Nature. Building on this past research, the most recent study assessed the impact of artificial intelligence in clinical deployment as part of a prospective, blinded and randomized controlled clinical trial.

"These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories. When applied to these clinical images, the algorithm identified specific features - related to the thickness of heart walls and the size of heart chambers - to efficiently flag certain patients as suspicious for having the potentially unrecognized cardiac diseases. "The algorithm identified high-risk patients with more accuracy than the well-trained eye of a clinical expert," said Ouyang.

Physician-scientists in the Smidt Heart Institute at Cedars-Sinai have created an artificial intelligence (AI) tool that can effectively identify and distinguish between two life-threatening heart conditions that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," said David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories.

Scientists from the Smidt Heart Institute at Cedars-Sinai have developed an artificial intelligence (AI) tool that can identify and distinguish between two life-threatening heart conditions that are often easy to miss--hypertrophic cardiomyopathy and cardiac amyloidosis. Their findings are published in JAMA Cardiology in a paper titled, "High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning." "Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis," the researchers wrote. "These two heart conditions are challenging for even expert cardiologists to accurately identify, and so patients often go on for years to decades before receiving a correct diagnosis," explained David Ouyang, MD, a cardiologist in the Smidt Heart Institute and senior author of the study. "Our AI algorithm can pinpoint disease patterns that can't be seen by the naked eye, and then use these patterns to predict the right diagnosis."

Over the years, robots have gotten quite good at identifying objects -- as long as they're out in the open. Discerning buried items in granular material like sand is a taller order. To do that, a robot would need fingers that were slender enough to penetrate the sand, mobile enough to wriggle free when sand grains jam, and sensitive enough to feel the detailed shape of the buried object. MIT researchers have now designed a sharp-tipped robot finger equipped with tactile sensing to meet the challenge of identifying buried objects. In experiments, the aptly named Digger Finger was able to dig through granular media such as sand and rice, and it correctly sensed the shapes of submerged items it encountered.