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Using AI to save the lives of mothers and babies

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As part of our SLAS Europe 2022 coverage, we speak to Professor Patricia Maguire from the University College Dublin about their AI_PREMie technology and how it can help to save mothers and babies lives. My name is Patricia Maguire, and I am a professor of biochemistry at University College, Dublin (UCD). Four years ago, I was appointed director of the UCD Institute for Discovery, a major university research institute in UCD, and our focus is cultivating interdisciplinary research. In that role, I first became excited by the possibilities of integrating AI into my research. I think there are two major obstacles to adopting AI in healthcare.


The Creepy TikTok Algorithm Doesn't Know You

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It's a truth universally acknowledged that "the algorithm" knows you better than you know yourself. A computer can supposedly predict whether you'll quit your job or break up with your partner. With 1,000 words of your writing, it can determine your age within four years. And no algorithm seems closer to omniscience than TikTok's, which is reportedly helping users discover their sexuality and unpack their childhood trauma. Whereas Facebook asks you to set up a profile, and hand over a treasure trove of personal information in the process, TikTok simply notices--or seems to.


AI Can Make a Difference on the Factory Floor If Biopharma Invests

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Artificial intelligence has been making a difference in the discovery lab for a decade. Recently, consulting group BCG suggested that AI played a pivotal role in the identification of more than 150 current drug candidates, 15 of which are in trials. And, according to a separate analysis by Deloitte, AI approaches have also been used in areas like clinical trials, supply chain management, and commercial launch. Instances of AI use in drug production are few and far between. And this is a missed opportunity, according to Kiefer Eaton, from industrial artificial intelligence developer Basetwo AI, particularly because drug makers already have many of the elements they need to benefit from AI. "AI has been widely adopted for drug development and has recently seen a lot of growth for commercial pharmaceutical activities. However, biomanufacturing remains underserved despite the plethora of data to leverage," he says.


HCG Hospitals adopts AI-driven smart digital scanning technology to improve cancer patient care

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HealthCare Global Enterprises Ltd (HCG) on Monday announced that it has deployed Sigtuple's AI100 making HCG the first hospital chain to equip the Hematopathology labs across its network with AI-powered screening solutions for cancer detection and disease management. According to the company's press statement, SigTuple's AI100 is the premier solution for AI-assisted digital hematopathology. It is also the only digital hematopathology solution available that is economical and robust enough for wide-scale adoption, it claimed. "As manual microscopy is still the standard in diagnosing several critical disorders like cancers, infections, etc., in the absence of a pathologist at site in laboratories outside urban areas, these samples need to be shipped to central reference laboratories for review. Apart from the logistic challenges and associated delays in turnaround times, there is also limited expertise available for providing high quality diagnostics at remote locations," it stated.


Shifting machine learning for healthcare from development to deployment and from models to data - Nature Biomedical Engineering

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In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance. This Review discusses the use of deep generative models, federated learning and transformer models to address challenges in the deployment of machine learning for healthcare.


Artificial Intelligence Detects New Family of Genes in Gut Bacteria

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Using artificial intelligence, UT Southwestern researchers have discovered a new family of sensing genes in enteric bacteria that are linked by structure and probably function, but not genetic sequence. The findings, published in PNAS, offer a new way of identifying the role of genes in unrelated species and could lead to new ways to fight intestinal bacterial infections. "We identified similarities in these proteins in reverse of how it's usually done. Instead of using sequence, Lisa looked for matches in their structure," said Kim Orth, Ph.D., Professor of Molecular Biology and Biochemistry, who co-led the study with Lisa Kinch, Ph.D., a bioinformatics specialist in the Department of Molecular Biology. Dr. Orth's lab has long focused on studying how marine and estuary bacteria cause infections.


La veille de la cybersécurité

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We all know technology is a driver of change. Much of the development and improvements we've seen in the healthcare industry today as compared to 20, 10, or even five years ago, have been a direct result of technological innovation. As technology continues to get smarter, faster and more reliable, it seems like the possibilities are endless. Artificial intelligence (AI), and especially machine learning (ML), are likely to have a tremendous impact on the future of the healthcare industry for patients, physicians and medical researchers. As one example, according to a 2016 study out of Johns Hopkins, medical errors stemming from individual and/or system-level mistakes were identified as the third-leading cause of death in the United States.


AI Business Transformation Playbook for Executives - DataScienceCentral.com

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I am delighted to present my new blog – AI Business Transformation Playbook for Executives. originally posted here. I get into the nuts-and-bolts of AI Systems Solutioning in this rather lengthy blog but the “First Ten Plays” at the end summarizes the key steps. I look forward to your thoughts and comments. – PG “AI, IoT &… Read More »AI Business Transformation Playbook for Executives


Artificial Intelligence in Medical Field

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For the past 2 years, the world has been in an era of pandemics because of Covid-19. Although now conditions have become better, still new variants of Covid are rising. Doctors are working 24x7 to tackle this problem. Besides Covid, there are other diseases for which doctors are needed, but can there be a solution, which can make the task of doctors easy? Yes, there is Artificial Intelligence.


Using GPUs to Discover Human Brain Connectivity - Neuroscience News

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Summary: Researchers developed a new GPU-based machine learning algorithm to help predict the connectivity of networks within the brain. A new GPU-based machine learning algorithm developed by researchers at the Indian Institute of Science (IISc) can help scientists better understand and predict connectivity between different regions of the brain. The algorithm, called Regularized, Accelerated, Linear Fascicle Evaluation, or ReAl-LiFE, can rapidly analyse the enormous amounts of data generated from diffusion Magnetic Resonance Imaging (dMRI) scans of the human brain. Using ReAL-LiFE, the team was able to evaluate dMRI data over 150 times faster than existing state-of-the-art algorithms. "Tasks that previously took hours to days can be completed within seconds to minutes," says Devarajan Sridharan, Associate Professor at the Centre for Neuroscience (CNS), IISc, and corresponding author of the study published in the journal Nature Computational Science.