AI Articles and Analysis in Healthcare

Deep Learning plays a vital role in the early detection of cancer. A study published by NVIDIA showed that deep learning drops error rate for breast cancer diagnoses by 85%. This was the inspiration for Co-Founders Jeet Raut and Peter Njenga when they created AI imaging medical platform Behold.ai. Raut’s mother was told that she no longer had breast cancer, a diagnosis that turned out to be false and that could have cost her life.

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.

In 1895, the German physicist, Wilhelm Röntgen, showed his wife Anna an X-ray of her hand. “I have seen my death,” she said. Medical imaging broke paradigms when it first began more than 100 years ago, and deep learning medical applications that have evolved over the past few years seem poised to once again take us beyond our current reality and open up new possibilities in the field.

In July of 2016, we sent out a series of survey questions to past guests who have been featured on the Emerj podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. In this article, we focus on responses to the following question:

Episode Summary: I remember reading an article in Scientific American years ago about a poster of a person looking in the direction people sitting in a school dining room, and that this poster would make people sitting in the dining room less likely to litter. This seems like an absurd example of holding people accountable for their actions, but as it turns out, there are a lot more serious consequences to ensuring behavior change through observation, and one area where this matters is medicine.
Today, there’s a major issue with people who don't adhere to their medical regimens, only to relapse or experience more serious symptoms later on. This week's guest, Cory Kidd, CEO of Catalia Health and known for his work at MIT on human-robotic interaction, is working to help solve this problem by developing a robot that adds some of that physical presence and accountability. This is likely one of many novel medical AI applications that we're likely to see roll out in healthcare over the next decade.

While there have been and continue to be innovative and significant machine learning applications in healthcare, the industry has been slower to come to and embrace the big data movement than other industries. But a snail's pace hasn't kept the data from mounting, and the underlying value in the data now available to health care providers and related service providers is a veritable goldmine. In this editorial, we provide an overview of where healthcare's big data actually comes from, and why providing robust data analytics services in this sector matters.

1 - Carnegie Mellon Receives $10 Million From K&L Gates To Study Ethical Issues Posed By Artificial Intelligence

As the term "machine learning" has heated up, interest in "robotics" (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?