Pharmaceuticals & Biotechnology


Five Strategies To Catalyze Conversational AI

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For many people, the general idea of artificial intelligence still conveys a dark, robot-master future. Yet simply add the word "conversational" in front of it, and suddenly it's a whole lot friendlier. Maybe that's why conversational AI is this year's "everywhere topic." It's a consensus pick in every technology forecast; a keynote subject at business, marketing, and IT conferences; a core capability claimed by software vendors of all sizes and specializations; and the destination of millions in venture funding. As evidenced by this year's CES conference, it's also reaching ever further into the connected home, vehicle, and workplace, at lightning speed.


Amplion's Machine Learning Platform Accelerates Precision Medicine Collaboration

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Amplion, a leading precision medicine intelligence company, has released Dx:Revenue, a groundbreaking software solution that enables test providers to identify ideal pharmaceutical partnership opportunities at the right time to advance precision medicine collaboration. Dx: Revenue is an extension of Amplion's core business intelligence platform that leverages proprietary machine learning to deliver tailored insights into pharma and test developer activities. The platform draws from more than 34 million evidence sources such as clinical trials, scientific publications, conference abstracts, FDA cleared and approved tests, lab developed tests, diagnostic and drug pipelines and more in real time, producing prioritized and timely partnering opportunities that are a precise match between a test provider's capabilities and pharma's specific needs. "Precision medicine has a problem," says Chris Capdevila, CEO, Amplion. "There is an insurmountable volume of information with the potential to drive the realization of precision medicine for patients, but accessing that information strategically, effectively and quickly to make the best pharma partnering decisions is beyond human scale. Our company was founded to address this issue by providing critical evidence-based intelligence that supports the strategic decisions pharmaceutical and test developers need to make to be successful."


How will human augmentation affect sustainability?

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If Facebook and Elon Musk's ambitions to directly connect human brains to machines are any indication, it seems we will become increasingly dependent on smart devices. Less bombastic than this proposal, but already widespread, are more mundane forms of augmentation – neural prostheses allow brains to control replacement body parts, artificial organs can be designed to specific bodies, and embedded devices like insulin pumps can intelligently support their hosts. With this is mind, we asked six experts the following question: How will technologically augmenting humans affect sustainability? The implications of human augmentation are so complex that it is difficult to succinctly assess what their implications will be for sustainability. There are, of course, potential benefits.


Machine learning much faster than humans at reading heart images

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Automated AI analysis of cardiac magnetic resonance images can be just as accurate as trained clinicians--but 186 times faster. Researchers say the technique can improve evaluations and reduce inconsistencies in interpretation. Cardiovascular MRI is the reference standard to assess the structure and function of a heart's left ventricle, a key imaging biomarker used for clinical decision making and as a clinical trial outcome measure. However, clinical analysis of these MRIs is significantly variable. The analysis is often not standardized, and there are avoidable inconsistencies when using human interpreters, such as noise or bias.


Novartis and Microsoft announce collaboration to transform medicine with artificial intelligence

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Disclaimer This press release contains forward-looking statements within the meaning of the United States Private Securities Litigation Reform Act of 1995 that can generally be identified by words such as "to transform," "multiyear," "commitment," "to found," "aims," "vision," "potential," "can," "will," "plan," "expect," "anticipate," "committed," or similar terms, or regarding the development or adoption of potentially transformational technologies and business models and the collaboration with Microsoft; or by express or implied discussions regarding potential marketing approvals, new indications or labeling for the healthcare products described in this press release, or regarding potential future revenues from collaboration with Microsoft or such products. You should not place undue reliance on these statements. Such forward-looking statements are based on our current beliefs and expectations regarding future events, and are subject to significant known and unknown risks and uncertainties. Should one or more of these risks or uncertainties materialize, or should underlying assumptions prove incorrect, actual results may vary materially from those set forth in the forward-looking statements. There can be no guarantee that the collaboration with Microsoft will achieve any or all of its intended goals or objectives, or in any particular time frame.


Doctors slam popular genetic tests for sparking confusion and fear in patients

Daily Mail - Science & tech

The NHS is having to'pick up the pieces' of growing use of cheap genetic tests, doctors warned last night. Popular DNA tests - which are widely available in pharmacists and online - can easily be misinterpreted, experts said. A panel of experts from Southampton University, Exeter University and Southampton Hospital said'direct-to-consumer' genetic tests are unreliable and leave people confused and uncertain. Writing in the British Medical Journal, they said genetic information is complex and even if people are shown to be at risk they need carefully walking through the results by a doctor – not left to panic at home. The writers, who include Professor Anneke Lucassen, president of the British Society for Genetic Medicine, said these tests should'absolutely not be used to inform health decisions without further scrutiny'.


How Artifical Intelligence Is Advancing Precision Medicine

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Artificial intelligence and machine learning have been utilized for years in the field of healthcare and continue to grow tremendously each year with its ability to advance medicine and discoveries in the industry. The term "precision medicine", sometimes referred to as "personalized medicine," is a relatively new term in the healthcare field but the idea has been around for many years in the industry. According to the U.S. National Library of Medicine, precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Precision medicine helps physicians determine more personalized treatments for patients considering individualized approaches instead of a blanketed approach for all patients. They do this by looking at a patient's genetic history, location, environmental factors, lifestyle and habits to determine a plan of action for treatment.


How Artifical Intelligence Is Advancing Precision Medicine

#artificialintelligence

Artificial intelligence and machine learning have been utilized for years in the field of healthcare and continue to grow tremendously each year with its ability to advance medicine and discoveries in the industry. The term "precision medicine", sometimes referred to as "personalized medicine," is a relatively new term in the healthcare field but the idea has been around for many years in the industry. According to the U.S. National Library of Medicine, precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Precision medicine helps physicians determine more personalized treatments for patients considering individualized approaches instead of a blanketed approach for all patients. They do this by looking at a patient's genetic history, location, environmental factors, lifestyle and habits to determine a plan of action for treatment.


Adopting AI in Health Care Will Be Slow and Difficult

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Artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome today's barriers to adoption, compliance, and implementation. Over the past few years, the U.S. Food and Drug Administration (FDA) has been taking incremental steps to update its regulatory framework to keep up with the rapidly advancing digital health market. In 2017, the FDA released its Digital Health Innovation Action Plan to offer clarity about the agency's role in advancing safe and effective digital health technologies, and addressing key provisions of the 21st Century Cures Act.


Computer science in service of medicine

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MIT's Ray and Maria Stata Center (Building 32), known for its striking outward appearance, is also designed to foster collaboration among the people inside. Sitting in the famous building's amphitheater on a brisk fall day, Kristy Carpenter smiles as she speaks enthusiastically about how interdisciplinary efforts between the fields of computer science and molecular biology are helping accelerate the process of drug discovery and design. Carpenter, an MIT senior with a joint major in both subjects, said she didn't want to specialize in only one or the other -- it's the intersection between both disciplines, and the application of that work to improving human health, that she finds compelling. "For me, to be really fulfilled in my work as a scientist, I want to have some tangible impact," she says. Carpenter explains that artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.