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What’s Next? Learning Communities in AI and Healthcare

Artificial intelligence is making a difference to healthcare, right now. For more information on how to get involved in this important and growing sector, take advantage of the resources outlined in this post, including the Dell EMC Machine Learning Knowledge Center, Dell EMC Customer Solution Centers and the Deep Learning Institute by Dell EMC. These have been created for the purpose of sharing expertise and offering guidance for those interested in deep learning.

Finding a solution for AI in Healthcare

Dell EMC Ready Solutions for AI are integrated systems with validated hardware and software stacks optimized to accelerate AI initiatives, shortening the time to architect a new solution by six to twelve months. A new insideBIGDATA special report series explores the intersection of AI and medicine, and this entry provides a series of solutions for AI in healthcare, including Dell EMC Ready Solutions for AI.

AI in Medicine: Developments in Radiology, Genomics & Diagnostics

Next-generation radiology tools may remove the need for tissue sampling and greatly expand the area of delivery. And the translational Genomics Research Institute’s (TGen) Center for Rare Childhood Disorders is using machine learning for genetic sequencing. Part of an insideBIGDATA special report series on AI and healthcare, this entry highlights some of the latest developments in AI in medicine, including progress in radiology, genomics and diagnostics.

Impressive Healthcare AI results for Vision Research & Cancer Treatment

This post explores the latest research and AI innovation in vision research and cancer treatment. Learn more about CSIRO’s bionic vision research, powered by the Dell EMC PowerEdge servers. And find out how Gustave Roussy, a leading European center for cancer treatment, sought to increase the processing capabilities of its bioinformatics platform to support more genome analyses per day while enhancing research programs, via a new insideBIGDATA special report.

Exploring the Application of AI Within Healthcare

AI is already having a profound impact on patient care. Machine learning and deep learning build the fundamentals to an AI system, and by taking a holistic approach to the data being generated within healthcare, AI-enabled systems can help to assess risk and facilitate improved human decision making. This insideBIGDATA special report article delves into the application of AI within healthcare, as well as the impact and challenges of these new tools. 

Medical Device Security: Ensuring Data Integrity

In this contributed article, technology writer and blogger Kayla Matthews suggests that one of the best ways to make healthcare smarter, more accurate and more engaging is by gathering data on a huge scale and then using it to gather insights into individual patient conditions as well as the effectiveness of treatments over a much broader scale.

The Future of EHRs, Big Data, and Patient Privacy

In this contributed article, freelance human Avery Phillips suggests that the volume and quality of patients records being transferred online is awesome in the original sense of the word. However, it also presents an ever-growing security risk, and standards will need to be held, with security researchers rising to the challenge of keeping the information as secure as possible and ensure companies maintain compliance with HIPAA.

8 Ways 8 Billion Data Points Can Lower Diabetes Costs

In this contributed article, David Conn, Chief Commercial Officer at Glooko, discusses how dealing with diabetes requires data, and how data science provides deep access to key aspects of chronic disease progression and treatment. Insights created by the data collected is key to improving overall care and avoiding risk in diabetes management.

Big Data and the Opioid Crisis

In this contributed article, freelance human Avery Phillips takes a look at the opioid crisis and how two major components of big data can help find a solution including patient behavioral analysis, and also prescription/outcome tracking.

3 Reasons to Consider Text Mining

A growing number of life science companies use text mining to gather important insights from vast amounts of published information. The results of mining projects inform a wide range of business activities including drug discovery, drug interactions, clinical trial development, drug safety monitoring and competitive intelligence. Download the new Copyright Clearance Center white paper that covers three of the top reasons to consider implementing text mining for life sciences companies.