Eisai Inc., the U.S. subsidiary of Eisai Co., Ltd., today announced the launch of Ella the Jellyfish, the first Amazon Alexa skill designed for those affected by Lennox-Gastaut Syndrome (LGS). Available free of charge, the Alexa skill was created with input from children living with LGS, a rare and severe form of childhood-onset epilepsy, their families and caregivers. When a child has a debilitating disease, everyday life for them, their families and caregivers can be challenging. LGS can be characterized by frequent and unpredictable seizures, limited speech and mobility, cognitive impairment, and developmental delays. Approximately 70 percent of patients with LGS will show cognitive impairment at diagnosis and more than 50 percent suffer behavioral issues including hyperactivity, sleep disturbances, aggression, and autistic symptoms.

Marlborough medical device maker Hologic announced Tuesday it has received U.S. Food & Drug Administration approval for its 3DQuorum Imaging Technology to reduces image interpretation time. The company said when combined with its high-resolution imaging technology, this new technology reduces the number of images needing to be reviewed without compromising image quality or accuracy through using artificial intelligence to find the best images. The number of images to be reviewed is reduced by 66%, according to Hologic, saving an average of one hour per eight hours of image interpretation time. The technology is available for use with existing and future Hologic 3D mammography systems.

This article originally appeared on aedit.com From smart speakers in the operating room to virtual diagnosis and treatment plans, The AEDITION looks at a few of the ways artificial intelligence is shaking up the medical industry. We may not be at the point where you overhear your surgeon saying, "Hey, Google, pass the scalpel," but artificial intelligence (AI) is gradually making its way into the healthcare industry and, by extension, dermatology and plastic surgery practices, too. Even in its limited use, AI is already helping providers offer their patients better care -- whether it's pre-op, in the OR, or during the recovery process. Here are three ways artificial intelligence is shaking up medicine.

Researchers have used a deep-learning algorithm to detect lung cancer accurately from computed tomography scans. The results of the study indicate that artificial intelligence can outperform human evaluation of these scans. The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%.

UCLA researchers have developed an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. The new system, described in a study published today in JAMA Network Open, helps interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurately or better as experienced pathologists. "It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).

Life sciences Artificial Intelligence products and services company, AIRA Matrix ("AIRA Matrix"), and microscope-based digital pathology platform Augmentiqs ("Augmentiqs"), announced the world's first pre-clinical deployment of deep-learning algorithms in an augmented reality microscope. The partnership between AIRA Matrix and Augmentiqs will allow pathologists to deploy deep-learning AI algorithms directly in their existing microscope. AIRA Matrix and Augmentiqs partnered together to deploy Artificial Intelligence ("AI") based pathology algorithms directly within the microscope. In this deployment, the deep learning solution for fatty liver and myopathy tissue samples highlighted and quantified the region of interest as the slide was on the microscope stage, with results presented in real-time to the pathologist as augmented reality within the microscope eyepiece. A Japanese organization sponsored the pre-clinical study, which took place at Integrated Laboratory Systems ("ILS"), a North Carolina Contract Research Organization.

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Description: A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

Students at the University of Alberta are getting hands-on experience with artificial intelligence with a new robotic arm. Donated to the university's department of computing science by Kindred AI, a Canadian-based artificial intelligence company, the use of the robotic arm in the classroom helps students get a sense of reinforcement learning. Reinforcement learning is a branch of artificial intelligence, says Rapum Mahmood, assistant professor at the U of A and former Kindred AI research lead. "In reinforcement learning, we study by letting the agent interact with the environment, so that it can take the right set of actions," said Mahmood. Usually, the study is done through computer simulations and board games but in real-world applications, a robotic arm is used.

This work provides a review of this literature. The motivation for this summary arose from our companion paper Ruf and W ang [2019]. There we continue th e discussions of this note; in particular, of potentially problematic data leakage when training ANNs to historic financial data. This paper is organised in the following way. Section 2 featu res Table 1, a summary of the literature that concerns the use of ANNs for nonparametric pricing (and hedging) of options. Section 3 provides a list of recommended papers from Table 1. Section 4 provides a n overview of related work where ANNs are applied in the context of option pricing and hedging, but not necessarily as nonparametric estimation tools. Section 5 briefly discusses various regularisation techniq ues used in the reviewed literature.