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— This report studies the global Healthcare Artificial Intelligence market, analyzes and researches the Healthcare Artificial Intelligence development status and forecast in United States, EU, Japan, China, India and Southeast Asia. This report focuses on the top players in global market, like IBM Watson Health AiCure Atomwise Cyrcadia Health Lifegraph Modernizing Medicine Sensely Zebra Medical Vision Sophia Genetics iCarbonX Welltok Butterfly Network APIXIO Pathway Genomics Enlitic Insilico Medicine Market segment by Application, Healthcare Artificial Intelligence can be split into Medical Imaging & Diagnosis Drug Discovery Other Some Points from Table of Content: Chapter One: Industry Overview of Healthcare Artificial Intelligence 1.1 Healthcare Artificial Intelligence Market Overview 1.1.1 Healthcare Artificial Intelligence Product Scope 1.1.2 Market Status and Outlook 1.2 Global Healthcare Artificial Intelligence Market Size and Analysis by Regions 1.2.1 United States 1.2.2 EU 1.2.3 Japan 1.2.4 China 1.2.5 India 1.2.6 Southeast Asia 1.3 Healthcare Artificial Intelligence Market by End Users/Application 1.3.1 Medical Imaging & Diagnosis 1.3.2 Drug Discovery 1.3.3 Other Chapter Two: Global Healthcare Artificial Intelligence Competition Analysis by Players 2.1 Healthcare Artificial Intelligence Market Size (Value) by Players (2016 and 2017) 2.2 Competitive Status and Trend 2.2.1 Market Concentration Rate 2.2.2 Product/Service Differences 2.2.3 New Entrants 2.2.4 The Technology Trends in Future Chapter Three: Company (Top Players) Profiles 3.1 IBM Watson Health 3.1.1 Company Profile 3.1.2 Main Business/Business Overview 3.1.3 Products, Services and Solutions 3.1.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.1.5 Recent Developments 3.2 AiCure 3.2.1 Company Profile 3.2.2 Main Business/Business Overview 3.2.3 Products, Services and Solutions 3.2.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.2.5 Recent Developments 3.3 Atomwise 3.3.1 Company Profile 3.3.2 Main Business/Business Overview 3.3.3 Products, Services and Solutions 3.3.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.3.5 Recent Developments 3.4 Cyrcadia Health 3.4.1 Company Profile 3.4.2 Main Business/Business Overview 3.4.3 Products, Services and Solutions 3.4.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.4.5 Recent Developments 3.5 Lifegraph 3.5.1 Company Profile 3.5.2 Main Business/Business Overview 3.5.3 Products, Services and Solutions 3.5.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.5.5 Recent Developments 3.6 Modernizing Medicine 3.6.1 Company Profile 3.6.2 Main Business/Business Overview 3.6.3 Products, Services and Solutions 3.6.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.6.5 Recent Developments 3.7 Sensely 3.7.1 Company Profile 3.7.2 Main Business/Business Overview 3.7.3 Products, Services and Solutions 3.7.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.7.5 Recent Developments 3.8 Zebra Medical Vision 3.8.1 Company Profile 3.8.2 Main Business/Business Overview 3.8.3 Products, Services and Solutions 3.8.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.8.5 Recent Developments 3.9 Sophia Genetics 3.9.1 Company Profile 3.9.2 Main Business/Business Overview 3.9.3 Products, Services and Solutions 3.9.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.9.5 Recent Developments 3.10 iCarbonX 3.10.1 Company Profile 3.10.2 Main Business/Business Overview 3.10.3 Products, Services and Solutions 3.10.4 Healthcare Artificial Intelligence Revenue (Value) (2012-2017) 3.10.5 Recent Developments 3.11 Welltok 3.12 Butterfly Network 3.13 APIXIO 3.14 Pathway Genomics 3.15 Enlitic 3.16 Insilico Medicine Make an enquire of this report @ http://www.orbisresearch.com/contacts/enquiry-before-buying/289447 . For more information, please visit http://www.orbisresearch.com/reports/index/global-healthcare-artificial-intelligence-market-size-status-and-forecast-2022


News Article | May 29, 2017
Site: www.prnewswire.com

Today, only 25% of rare diseases are accurately diagnosed1. With SOPHiA's applications for exome analysis, this is set to change. The exome is the protein-coding region of the human genome, which represents just 1% of the genome but contains approximately 85% of known disease-causing genetic variants2. SOPHiA takes exome sequencing to new heights, allowing for unmatched analytical performances to detect, annotate and pre-classify disease-related genetic variants over all protein-coding regions of the human genome. Exome sequencing generates large amounts of sequenced data. The problem is that reporting variants depends on the algorithms used to sift through this data, leading to a substantial discordance in variants description between the different annotation tools and their descriptions in functional and clinical databases. Consequently, clinicians can confound variant matching, which is a critical step in variant classification. SOPHiA now overcomes variant annotation tools' limitations by annotating variants in a way that helps clinicians better interpret genomic data and achieve more accurate and precise clinical care. Based on pattern-recognition technologies, SOPHiA features a database search engine that guarantees the identification and retrieval of the matching variants regardless of their representations, such as Indels (insertion or the deletion of bases in the DNA) aligned differently, or complex variants. Being already used by over 830 experts (i.e. biologists, pathologists, and geneticists) across 285 hospitals, SOPHiA's unique approach to knowledge sharing makes the experience of an expert in one hospital scalable for the diagnosis of patients in other hospitals. In fact, the more variant interpretations are performed, the more SOPHiA is trained, and the better genetic variants are pre-classified according to the disease types. With the steady increase of both clinically-relevant genes and genetic variants to consider in diagnostic, this technology is now a must to help ensure all patients are being diagnosed at the same level, no matter their location. Dr. Reinhard Hiller from the Centre for Proteomic and Genomic Research Artisan Biomed Laboratory based in Cape Town, South Africa, explained how WES helps save precious time and resources, "Sophia Genetics' Whole Exome Solution (WES) serves as an excellent benchmark for our laboratory as it detects and validates a more comprehensive variant list. Sophia DDM® analytical platform is user-friendly and easy to navigate, making it possible for a user to be hands-on from benchtop to variant calling." In less than three years, SOPHiA has made genomics for routine clinical diagnostic a reality in over 285 hospitals from 50 countries across the globe. Using AI and leveraging knowledge-sharing to create a collective intelligence, Sophia Genetics continues its mission to democratize Data-Driven Medicine. With faster, more affordable, and more accurate results, the AI-powered exome solutions by Sophia Genetics represent the next frontier in Data-Driven Medicine, and the most efficient diagnostics tool for clinicians faced with unclear phenotypic data. Global leader in Data-Driven Medicine, Sophia Genetics is a technology company which has developed SOPHiA, the most advanced collective artificial intelligence for clinical genomics, helping healthcare professionals better diagnose and treat patients. For more information, please visit: www.sophiagenetics.com  @SophiaGenetics  @JurgiCamblong


Kokotas H.,Sophia Genetics | Petersen M.B.,Sophia Genetics
Clinical Genetics | Year: 2010

Aniridia is a severe, congenital ocular malformation inherited in an autosomal-dominant fashion with high penetrance and variable expression. Eye morphogenesis in humans involves a molecular genetic cascade in which a number of developmental genes interact in a highly organized process during the embryonic period to produce functional ocular structures. Among these genes, paired box gene 6 (PAX6) has an essential role as it encodes a phylogenetically conserved transcription factor almost universally employed for eye formation in animals with bilateral symmetry, despite widely different embryological origins. To direct eye development, PAX6 regulates the tissue-specific expression of diverse molecules, hormones, and structural proteins. In humans, PAX6 is located in chromosome 11p13, and its mutations lead to a variety of hereditary ocular malformations of the anterior and posterior segment, among which aniridia and most probably foveal hypoplasia are the major signs. Aniridia occurs due to decreased dosage of the PAX6 gene and exists in both sporadic and familial forms. The mutations are scattered throughout the gene and the vast majority of those reported so far are nonsense mutations, frameshift mutations, or splicing errors that are predicted to cause pre-mature truncation of the PAX6 protein, causing haploinsufficiency. Here we review the data regarding the mechanisms and the mutations that relate to aniridia. © 2010 John Wiley & Sons A/S.


Douzgou S.,Sophia Genetics | Petersen M.B.,Sophia Genetics
Clinical Genetics | Year: 2011

Cohen syndrome (CS) (OMIM#216550) is an uncommon autosomal recessive developmental disorder that has been attributed to mutations in the COH1 gene in at least 200 patients of diverse ethnic background so far. The clinical heterogeneity of CS is evident when comparing patients of different ethnic backgrounds, especially when evaluating specific system phenotypes separately, such as the ophthalmic and central nervous systems. We reviewed the available clinical data on CS cohorts of patients who share a founder effect and demonstrated that most features associated so far with CS are less than those always present in the patients who share a founder mutation thus representing clinical heterogeneity. Furthermore, there is a wide clinical variability of CS in the distinct founder mutation cohorts, the Finnish, Greek/Mediterranean, Amish and Irish travelers. The Greek/Mediterranean founder mutation is correlated to a CS phenotype characterized by specific and persistent skeletal features, corneal changes, periodontal disease, a distinct neurocognitive phenotype for the high recurrence of autism and non-verbal communication and inconstant microcephaly. © 2011 John Wiley & Sons A/S.


A method to manage raw genomic data (SAM/BAM files) in a privacy preserving manner in a biobank. By using order preserving encryption of the reads positions, the method provides a requested range of nucleotides to a medical unit, without revealing the locations of the short reads (which include the requested nucleotides) to the biobank. The method prevents the leakage of extra information in the short reads to the medical unit by masking the encrypted short reads at the biobank. That is, specific parts of the genomic data for which the medical unit is not authorized or the patient prefers to keep secret are masked at the biobank, without revealing any information to the biobank.


A method to manage raw genomic data (SAM/BAM files) in a privacy preserving manner in a biobank. By using order preserving encryption of the reads positions, the method provides a requested range of nucleotides to a medical unit, without revealing the locations of the short reads (which include the requested nucleotides) to the biobank. The method prevents the leakage of extra information in the short reads to the medical unit by masking the encrypted short reads at the biobank. That is, specific parts of the genomic data for which the medical unit is not authorized or the patient prefers to keep secret are masked at the biobank, without revealing any information to the biobank.


News Article | July 8, 2014
Site: www.finsmes.com

The round was led by Dr Mike Lynch’s Invoke Capital, Swisscom and Endeavour Vision. Led by Jurgi Camblong, CEO, Sophia Genetics is a pioneer in Data-Driven Medicine, a crossover field that requires deep expertise in Next Generation Sequencing (NGS), combined with accurate and scalable predictive algorithms to diagnose genetic diseases. With this investment, the company gains access to Invoke’s portfolio company Genalys, whose Cambridge-based mathematicians apply big data approaches to genomic information. In three years, Sophia Genetics has developed bioinformatics pipelines and visualization tools for more than 70 commercial panels, proprietary custom panels and is extending that capability to whole exomes and genomes. The company obtains CE-IVD marking for each supported pipeline and has received ISO 13485 certification.


News Article | February 28, 2017
Site: www.techrepublic.com

Over 14 million new cancer cases worldwide were reported in 2012, with the rate of occurrence expected to jump by approximately 70% over the next two decades, according to the World Health Organization. Cancer patients are often treated with chemotherapy and various types of drugs, but the results of these treatments aren't uniform in effectiveness, which is why it's imperative for hospitals, clinics, and doctors to make the best drug treatment choices for each individual patient. Getting drug therapies right is an area where digitalized genomics data can help. "The technique that we use for this is genomic sequencing," explained Dr. Jurgi Camblong, cofounder of Sophia Genetics, a provider of artificial intelligence that pinpoints the genomic code mutations behind cancers and rare disorders to assist physicians and healthcare institutions in prescribing optimal drug treatments for their patients. Today, 240 hospitals in 39 countries use the Sophia platform. "What the technology does is spot variations in different genetic codes so we can use historical data that aids in prescribing the best combination of drugs to treat a particular cancer or condition in an individual patient," said Camblong. "This is next-generation genomic sequencing, and it is used in two different areas: chronic hereditary disorders and oncology. By using the algorithms that are part of our artificial intelligence, we can spot the origin of a genetic mutation causing a cancer or a particular condition and then give an idea of what the best drug treatment would be to the attending physician." An example is lung cancer, where treatment in the past was prescribed based upon the patient's tissue type instead of on a particular genetic mutation. By using genetic sequencing and mutation detection instead of tissue analysis, physicians can now identify the genetic events that caused the condition in the first place, and not just treat symptoms. "The more we understand the molecular events at the origin of the disease or disorder, the better we can understand the effects of what certain combinations of drugs are likely to be," said Camblong. "The process begins with the extraction of the patient's DNA via a blood draw or biopsy, said Camblong. "The hospital then uses molecular biology processes to prepare the samples and subsequently digitizes them using a DNA sequencer. The resulting genomic data is then submitted to the company AI on the Sophia DDM Software as a Service (SaaS) platform, which digs around to identify the patient's genomic mutations. The more hospitals use the analytics platform, the more patients' genomic profiles are accumulated, and the smarter the AI gets." "Without this technology, the process of determining a drug treatment takes about two days work, and in some cases can take several months when using old technologies," said Camblong. In contrast, healthcare professionals who use Sophia for genetic sequencing and analysis can get drug treatment regime recommendations for individual patients in one day. When developing the solution, one challenge Sophia and its healthcare customers faced was guaranteeing patient privacy. To ensure privacy, references to individual patients are stripped off of all treatment records so that the data is fully anonymized before it is ever admitted to Sophia's data repository. One benefit to the Sophia system being a SaaS-based solution is that even smaller hospitals and clinics can afford the technology, which on average costs $50-$200 per genetic evaluation. A second advantage that a SaaS-based platform brings is democratization of information, because drug outcomes for different cancers and conditions can be shared globally. "This democratization of the data is extremely important," said Camblong, "because not every hospital has the clinical expertise to prescribe the optimal treatments for different conditions. Because we can share data and encourage collaboration through the platform, these clinics now have access to experts and results from around the world."


LAUSANNE, Switzerland, Nov. 10, 2016 /PRNewswire/ -- Today, at the AMP 2016 Annual Meeting, Sophia Genetics, global leader in Data-Driven Medicine, and world-class Next-Generation Sequencing (NGS) assay developer ArcherDX, announced the signing of a new partnership to combine their...

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