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Health & Medicine


Machine learning helps distinguishing diseases - Innovation Origins

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Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes. In biomedicine, one often speaks of the molecular mechanisms of a disease. This refers to changes in the regulation of genes, proteins or metabolic pathways at the onset of illness. The goal of stratified medicine is to classify patients into various subtypes at the molecular level in order to provide more targeted treatments, wrties the Technical University of Munich in a press release.


How an AI robot can help seniors battle loneliness - MedCity News

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Powered by AI, ElliQ is a voice-operated robotic care companion designed to foster independence and provide support for older adults. About 1 in 4 adults 65 and older are considered to be socially isolated, putting them at increased risk for a wide range of health conditions, from dementia to heart disease and stroke. Many don't have family and friends who live nearby or visit regularly; and the number of professional caregivers is failing to keep up with demand as the U.S. population ages. Responding to the need, the New York State Office for the Aging announced Wednesday that it is partnering with Intuition Robotics to bring an AI robotic care companion into the homes of 800 older adults as part of the state's efforts to battle social isolation and support aging in place. NYSOFA is working with local offices for the aging and other partners to identify older adults who would most benefit from ElliQ by Intuition Robotics, which the Israeli company describes as first-ever proactive and empathetic AI robotic care companion.


BGI Releases 1st Panoramic Atlases of Life

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International scientists led by China's BGI•Research released the world's first panoramic spatial atlases of life on May 4, examining the cellular dynamics of organisms at different developmental stages and providing potentially significant new information for disease treatment, development and aging, and an improved understanding of biological evolution. In a series of studies published in Cell Press journals, members of Spatio-Temporal Omics Consortium (STOC), an international scientific consortium, used the spatially resolved transcriptomics technology Stereo-seq to produce spatio-temporal cellular maps of mice, small fruit flies, zebrafish and the Arabidopsis (thale cress) plant. The papers demonstrate how Stereo-seq has achieved a major breakthrough in spatial resolution and panoramic field of view, enabling analysis of the distribution and placement of molecules and cells in situ, and over time. Over 80 scientists from leading universities in 16 countries have so far collaborated as part of STOC focusing on using spatially resolved, cellular resolution omics technologies to map and understand life. It builds on the achievements of single-cell sequencing, elevating it to the next level by enabling scientists to track a cell's precise location and how it interacts with its neighbors.


How Big Data, AI, IoT and Deep Learning Are Powering Modern Healthcare

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Big data, artificial intelligence (AI), internet of things (IoT) and deep learning (DL) are revolutionizing modern healthcare post pandemic. After having made remarkable improvements in finance, retail and marketing, big data, artificial intelligence, internet of things (IoT) and deep learning are now transforming healthcare. The volume of data involved in healthcare studies and analysis makes it a perfect use-case for these ground breaking technologies. Healthcare industry handles an immense load of data that is piling up every day. Sooner or later, we will need big data tools to transform healthcare information into relevant insights that can help the development of health services.


In the future, these five jobs will be replaced by Artificial Intelligence

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When we think about artificial intelligence taking over human activities, this is the first thing that springs to mind. Artificial intelligence is now widely regarded as one of the most transformative technologies of our time. Technology offers immense potential for driving corporate growth, automating industrial processes, providing insightful results, and using targeted ads, among other things. Artificial intelligence's practical applications are no laughing matter. Many people have painted AI in a bad light because of the level of automation it causes.


How Do Patients Benefit From Artificial Intelligence

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Artificial intelligence in healthcare has come a long way. The use of computers has advanced significantly over the past few years. Today, sophisticated machines have been developed to perform human tasks like analyzing and interpreting data and assisting with problem-solving. While machine learning (ML) has been widely used in many industries, the use and application of Artificial Intelligence (AI) in healthcare is still relatively new. It is only recently that we have seen AI move from the world of academics and research laboratories to hospitals.


How you can create value in an intelligent health ecosystem

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The health care revolution is not just an opportunity but an urgent and essential need. Our existing health care models are not sustainable in the long run. The cost of health spending continues to rise with the rapid worldwide growth of costly chronic diseases. Meanwhile, the global health care workforce faces a predicted shortfall of 18 million health workers by 2030, a gap which will accelerate the necessary adoption of digital technologies. Yet while these trends are widely acknowledged, health care organizations and stakeholders need to recognize that we now also have the tools for transformation, which will not only drive efficacy of care and personalization, but also, and equally importantly, better access and efficiency.


Aidoc and Gleamer Partner To Expand the Use of AI in Medical Imaging

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This partnership will help health systems address the increasing volume of medical images and the worldwide radiologist labor shortage. Integration of Boneview into Aidoc's AI platform will give many more clinicians access to a tool to help them identify fractures in limbs, pelvis, thoracic and lumbar spine, and rib cage. Aidoc's end-to-end AI platform already includes numerous third-party AI vendors including Imbio, Riverain, Subtle, Icometrix and ScreenPoint. Over 152 million X-rays are performed every year in the US. Although there are about 37,000 radiologists in the US, they are not evenly distributed.


Making the most of MLOps

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When companies first start deploying artificial intelligence and building machine learning projects, the focus tends to be on theory. Is there a model that can provide the necessary results? How can it be built? How can it be trained? But the tools that data scientists use to create these proofs of concept often don't translate well into production systems.


Novel AI algorithm developed for assessing digital pathology data

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Digital pathology is an emerging field which deals with mainly microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them. Seeing a need, researchers from Boston University School of Medicine (BUSM) have developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors. We are developing novel AI-based methods that can bring efficiency to assessing digital pathology data.