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."

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.

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.

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.

Developments in Artificial Intelligence and Machine Learning (AI/ML) are rapidly creating competitive advantages for established and emerging companies across many industries. The healthcare sector is no exception, as health services, biopharmaceuticals and medical device firms are enjoying the benefits and experiencing the disruption of AI/ML. In fact, Statista projects that the market for AI in healthcare will climb from just over one billion in 2017 to more than $28 billion U.S. dollars by 2025. Our clients rely on IMS and our extensive network of best-in-class experts to provide them the foremost experts to consult, opine and often testify regarding emerging technologies, policies, and areas of disruption in critical strategic projects and complex commercial litigation. Stu Lipoff, a sought-after expert in healthcare data, cybersecurity and data privacy has been relied upon by IMS clients for many high-profile cases, notes that the introduction of Software as a Medical Device (SaMD) and AI/ML in the space are leading to complex questions.

We're looking back at the highlights from the Digital Pathology and AI meeting in 2018 as we anticipate this year's Digital Pathology and AI Congress in December. This second post in our mini-series reviews Marylin Bui's keynote presentation where she explained how the combination of Digital Pathology and Artificial Intelligence (AI) holds huge potential for patient care. In the first keynote address for the 5th Digital Pathology & AI Congress (Europe), Professor of Pathology, Marilyn Bui, focused on how digital pathology is impacting on precision medicine. During her address, 'Unleash the Power of Digital Pathology and Artificial Intelligence for Precision Medicine', the professor outlined how digital pathology provides connectivity and accessibility by combining with image analysis and AI; leading to improved quality and efficiency, and transforming pathology data into clinically actionable knowledge to help deliver precision medicine. The next phase of advanced imaging analysis combined with AI is a'game changer' in advancing the field, she added.

Healx, a company using artificial intelligence (AI) to discover new drug treatments for rare diseases, has raised $56 million in a series B round of funding led by Atomico, with participation from Intel Capital, Balderton Capital, Global Brain, Btov Partners, Amadeus Capital Partners, and Cambridge Innovation Capital's Jonathan Milner. Founded out of Cambridge in 2014, Healx's core AI Platform -- Healnet -- applies a range of machine learning techniques to public and proprietary data sources, covering literature, clinical trials, patents, drug targets, chemical structures, symptoms, and more. Part of this process involves using natural language processing (NLP) to extract insights and knowledge from all the published sources around specific diseases. The culmination of all this data, Healx CEO and cofounder Dr. Tim Guilliams said, is a knowledge graph of rare diseases that could help pharmacologists or biologists unearth effective new treatments that would otherwise be much more difficult to spot. "We use a variety of machine learning algorithms to solve the many tasks necessary to predict drug treatments and translate them in the clinic effectively," Guilliams told VentureBeat.

The more medications a patient takes, the greater the likelihood that interactions between those drugs could trigger negative side effects, including long-term organ damage and even death. Now, researchers at Penn State have developed a machine learning system that may be able to warn doctors and patients about possible negative side effects that might occur when drugs are mixed. In a study, researchers designed an algorithm that analyzes data on drug-drug interactions listed in reports -- compiled by the Food and Drug Administration and other organizations -- for use in a possible alert system that would let patients know when a drug combination could prompt dangerous side effects. Let's say I'm taking a popular over-the-counter pain reliever and then I'm put on blood pressure medicine, and these medications have an interaction with each other that, in turn, affects my liver. Essentially, what we have done, in this study, is to collect all of the data on all the diseases related to the liver and see what drugs interact with each other to affect the liver." Drug-drug interaction problems are significant because patients are frequently prescribed multiple drugs and they take over-the-counter medicine on their own, added Kumara, who also is an affiliate of the Institute for CyberScience, which provides supercomputing resources for Penn State researchers. "This study is of very high importance," said Kumara. "Most patients are not on one single drug.

"One of the most exciting things about deep learning reconstruction is how it redefines the usual negotiation between exam time and image quality. This could lead to significant downstream value for imaging operations and for patient experience," said Christopher Hess, MD, Chair of the Department of Radiology and Biomedical Imaging at UCSF. SubtleMR delivers a significant improvement in the quality of noisy images, which is particularly beneficial for patients who have difficulty holding still for long periods of time. Artifact-ridden images and the need for re-scans are a challenge for both patients and physicians. SubtleMR integrates seamlessly into the radiology workflow, and it is compatible with any brand of MRI scanner and PACS.

Omega Medical Imaging, manufactures of Artificial Intelligence Fluoroscopy/Cine (AIF/C) Imaging systems, just announced the Food and Drug Administration 510 (k) clearance of FluoroShield with their 2020 Cardiac Flat Panel Detector. The unique FluoroShield system allows for auto collimation during interventional fluoro or cine cases while maintaining a perspective of surrounding anatomy. The blended image incorporates a lower frequency refresh of the peripheral image area. This combined image (live fluoroscopy or cine of ROI background refreshed at a rate of once or twice per second) increases the quality of information presented during interventional procedures. Brian Fleming, President of Omega Medical Imaging states, "Until now products on the market have only been able to manage radiation to patients and staff. FluoroShield is the only system in the world that provides an actual reduction in dose. The impact of this groundbreaking solution for patients and healthcare providers is substantial. I am very grateful to be a part of a team that pushes the envelope in the development of safer healthcare solutions."