AI Articles and Reports in Healthcare

Explore articles and reports related to artificial intelligence in healthcare, including coverage of diagnostics, pharma, drug development, medical billing, and more.

machine learning in pharma and medicine

7 Applications of Machine Learning in Pharma and Medicine

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. We provide a breakdown of several of these pioneering applications, and provide insight into areas for continued innovation.

Deep Learning Applications in Medical Imaging 9

Deep Learning Applications in Medical Imaging

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.

How to Apply Machine Learning to Business Problems 4

Machine Learning Industry Predictions: Expert Consensus

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:

How to Apply Machine Learning to Business Problems

Five Year Trends in Medical AI Applications

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.

Google Algorithm Disrupts Medical Field, Intel Launches Automated Driving Group, and More  - This Week in Artificial Intelligence 12-02-16 13

Google Algorithm Disrupts Medical Field, Intel Launches Automated Driving Group, and More – This Week in Artificial Intelligence 12-02-16

1 - An AI Ophthalmologist Shows How Machine Learning May Transform Medicine

Google researchers are disrupting the medical field with a new algorithm capable of diagnosing eye disease. The team has been collaborating with the Aravind Medical Research Foundation in India to test-drive a deep-learning algorithm, trained to spot and diagnose a common cause of blindness known as diabetic retinopathy. The method overlaps with the Google's machine learning approach for labeling an influx of web images. While AI systems have had mixed success in medical diagnostics in past trials, this new algorithm has so far matched or surpassed ophthalmologists in diagnosing the condition in patients. This type of automated detection could not only increase reliability and efficient of diagnoses, but also serve patients in places where human expertise is limited. Google Researcher Lily Peng voiced that one of the present challenges is developing a a system that can explain its findings.

Google Algorithm Disrupts Medical Field, Intel Launches Automated Driving Group, and More  - This Week in Artificial Intelligence 12-02-16 11

DeepMind Partners with UK to Streamline Health Services, Chinese Researchers Claim AI-Powered System can Spot Criminals, and More – This Week in Artificial Intelligence 11-25-16

1 - Is DeepMind’s Health-Care App a Solution, or a Problem?

A new app from Google's DeepMind called Streams will provide access to patients' histories and test results to hospitals in the UK. After signing a five-year contract with UK's National Health Service, DeepMind is now privy to 1.6 million + patients' healthcare information registered with one of Royal Free NHS Trust’s three London hospitals. The contract and app is a trial run into helping streamline the healthcare provider and delivery system, and DeepMind claims that the system could save over half-a-million hours per year for healthcare administrators. Critics have been quick to turn an eye on the large amount of data that DeepMind would otherwise not have access to, but an analysis of this initial foray may prove to be imperative as machine and deep learning enter the prized but sheltered healthcare sector.

Healthcare

Explore articles and reports related to artificial intelligence in healthcare, including coverage of diagnostics, pharma, drug development, medical billing, and more.