AI Articles and Analysis about Research and development

Episode Summary: This episode explores the ways in which artificial intelligence has the potential to revolutionize the field of medicine. This week's guest, Turbine.ai's Dr. Kristóf Zsolt Szalay speaks to this topic, discussing research that hopes to create automated learning networks and algorithms designed to predict the development of human cells in response to drugs. This technological innovation would make it possible for near-instantaneous simulations to be run, allowing optimal combinations and optimal doses of drugs to be pinpointed and distributed to patients.

Episode summary: In this episode, we talk to Murali Aravamudan, Founder and CEO of AI-driven drug discovery startup Qrativ, a joint venture by the Mayo Clinic and biotech/data science firm nference. Murali and I discuss the surge of medical information and data in the medical industry, the role of artificial intelligence in developing drugs for treatments to various diseases, and the future of AI in drug discovery.

Discovering a new drug is a long, expensive and often haphazard process. Thousands of compounds are subject to a progressive series of tests, and only one might turn out to be a viable drug. Any tool which can speed up just one of these steps in this long multi-step process would have big implications down the entire chain. This is why some of the largest pharmaceutical companies are turning to AI to help the process.

Genomics is a branch of molecular biology focused on studying all aspects of a genome, or the complete set of genes within a particular organism. Today, machine learning is playing an integral role in the evolution of the field of genomics.

Episode summary: Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles.

A company by company examination of the top car makers public investment and statements by their top executives makes it clear that most car companies are betting that artificial intelligence utilized in self-driving will be inevitable, and they're all jumping in with investment and initiatives.

Episode Summary: Venture investing in AI healthcare applications has been on the uptick and is directly related to the subject of this week's episode: just how the healthcare industry is (and isn't) being impacted by innovations in AI technology. Guest Steve Gullans of Boston-Based Excel Venture Management talks about some of the various healthcare-related ML and AI applications that he sees being brought to light, and touches on which innovations have a better chance of getting blocked and redirected by parties of interest and those that have more promise in being accepted and rolled out sooner.

When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals.
Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality.
At Emerj, the AI Research and Advisory Company, we research how AI is impacting the pharmaceutical industry as part of our AI Opportunity Landscape service. Global pharma companies use AI Opportunity Landscapes to find out where AI fits at their company and which AI applications are driving value in the industry.
In this article, we use insights from our research to provide a breakdown of several of the pioneering applications of AI in pharma and areas for continued innovation.