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This category is meant to tell you all about using modern Artificial Intelligence technologies in Finance sector of our life.

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Artificial Intelligence in the Pharmaceutical Industry 950×540

Artificial Intelligence in the Pharmaceutical Industry – An Overview of Innovations

Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records.

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.
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.

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AI for Mobile Medical Diagnostics - 4 Current Applications

AI for Mobile Medical Diagnostics – Current Applications

Before getting into this report, we have to inform readers that none of the companies discussed below claim to offer software that provides diagnostics, except Cognoa, which has FDA approval to call itself a diagnostic tool. We suspect this is because these companies are not legally allowed to do so. We usually don't refer to a dictionary to determine what constitutes a concept, preferring to create our own informed definitions, such as in our What is Machine Learning? piece, but Merriam Webster defines "diagnosis" as the following: "the art or act of identifying a disease from its signs and symptoms."

Machine Learning for Medical Diagnostics 950×540

Machine Learning for Medical Diagnostics – 4 Current Applications

Medical diagnostics are a category of medical tests designed to detect infections, conditions and diseases. These medical diagnostics fall under the category of in vitro medical diagnostics (IVD) which be purchased by consumers or used in laboratory settings. Biological samples are isolated from the human body such as blood or tissue to provide results. Today, AI is playing an integral role in the evolution of the field of medical diagnostics.

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.

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Artificial Intelligence in Healthcare - A Comprehensive Overview

Artificial Intelligence in Healthcare – A Comprehensive Overview

Various use cases and applications for AI and machine learning in the healthcare industry are proposed more frequently now than ever. Healthcare leaders may find it difficult to keep up with where AI is being applied in their sector.

Machine Learning in Healthcare: Expert Consensus from 60+ Executives

Machine Learning in Healthcare: Expert Consensus from 50+ Executives

The last few years have yielded a tremendous amount of attention at the intersection of AI and healthcare, from DeepMind's partnership with the UK's National Health Service to IBM's continued pushes into areas of genomics and drug discovery. From the perspective of healthcare executives, however, many important questions are left unanswered and rarely addressed in detail:
What difference are healthcare's machine learning innovations likely to make in the lives of patients?
What disruptions should healthcare executives prepare themselves for now?
How will the healthcare industry operate differently in 5 or 10 years into the future?
We surveyed over 50 executives of healthcare companies leveraging AI. We aimed to do the hard work of separating the companies actually applying AI from those who use it as a buzzword (over 15 of our initial survey responses were turned down due to lack of evidence of real AI in use), presenting important predictions and industry insights in clear and interactive charts and graphs.
The following research article is broken down into five sections:

Artificial Intelligence in Healthcare 950×540

Machine Learning Healthcare Applications – 2018 and Beyond

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

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Top 3 Most Funded Start Ups

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