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Healthcare in Israel - Wikipedia

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Healthcare in Israel is universal and participation in a medical insurance plan is compulsory. All Israeli residents are entitled to basic health care as a fundamental right. The Israeli healthcare system is based on the National Health Insurance Law of 1995, which mandates all citizens resident in the country to join one of four official health insurance organizations, known as Kupat Holim (קופת חולים - "Sick Funds") which are run as not-for-profit organizations and are prohibited by law from denying any Israeli resident membership. Israelis can increase their medical coverage and improve their options by purchasing private health insurance.[1] In a survey of 48 countries in 2013, Israel's health system was ranked fourth in the world in terms of efficiency, and in 2014 it ranked seventh out of 51.[2] In 2015, Israel was ranked sixth-healthiest country in the world by Bloomberg rankings[3] and ranked eighth in terms of life expectancy. During the Ottoman era, health care in the region of Palestine was poor and underdeveloped. Most medical institutions were run by Christian missionaries, who attracted the indigent by offering free care. In the late nineteenth century, as the Yishuv, the pre-state Jewish community, began to grow in the wake of the First Aliyah, the Jews attempted to establish their own medical system. In 1872, Max Sandreczky, a German Christian physician, settled in Jerusalem and opened the first children's hospital in the country, Marienstift, which admitted children of all faiths.[4] The Jewish agricultural settlements, financially backed by Baron Edmond de Rothschild, hired a physician who traveled between the communities and ran a pharmacy in Jaffa which he visited twice a week.[5]


AI achieves near-human accuracy in diagnosing cancer

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New research suggests that computer models could help doctors achieve greater accuracy in the diagnosis of cancer and other diseases. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed an artificial intelligence (AI) system which is able to train computers to analyse pathologic image data [PDF]. The scientists hope that the programme could one day aid in diagnosing disease. 'Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition,' explained Andrew Beck, director of bioinformatics at the Cancer Research Institute at BIDMC and associate professor at HMS. He added: 'This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs.'


Better Together

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Pathologists have been largely diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. But new work suggests that computers can help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed. A research team from Harvard Medical School and Beth Israel Deaconess Medical Center and recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, HMS associate professor of pathology and director of bioinformatics at the Cancer Research Institute at Beth Israel Deaconess. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs."


Automated Artificial Intelligence Speeds Identification of Blood Pathogens GEN

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Scientists in Boston have developed an automated artificial intelligence (AI)-guided microscopy system that can help diagnose serious bloodstream infections (BSIs) quickly and accurately. The technology, which uses a trained convolutional neural network (CNN) to recognize the different shapes and distribution of pathogenic bacteria, could help to speed diagnosis and potentially save patient lives, as well as address the current lack of trained microbiology technologists, suggest its developers at Harvard Medical School and Beth Israel Deaconess Medical Center (BIDMC).


Artificial intelligence achieves near-human performance in diagnosing breast cancer

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A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs." The Beck lab's approach was recently put to the test in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI), which involved examining images of lymph nodes to decide whether or not they contained breast cancer. The research team of Beck and his lab's post-doctoral fellows Dayong Wang, PhD and Humayun Irshad, PhD, and student Rishab Gargya, together with Aditya Khosla of the MIT Computer Science and Artificial Intelligence Laboratory, placed first in two separate categories, competing against private companies and academic research institutions from around the world.