Natural Language Processing

Although the term “evidence-based medicine” (EBM) first appeared in print in the early 1990s, the history of this now-popular approach to clinical practice goes back much further. In the mid-18th century, James Lind, a Scottish naval physician, experimented with citrus-based scurvy treatments on several comparable groups of sick sailors. And there is evidence of what can be loosely called EBM stretching all the way back to the ancient Greeks.

The modern approach to EBM — which bases medical decisions on the evidence summarized in systematic literature reviews (SLRs), which themselves are based on analysis of randomized controlled trials (RCTs) of…


Big data, artificial intelligence (AI), and machine learning (ML) are already impacting healthcare in numerous ways — from diagnostics to treatment, to everyday administrative processes, including scheduling or tracking regulatory compliance. But perhaps the most noticeable, at least for patients, is how AI is helping to revolutionize patient engagement and adherence.

AI in Healthcare
AI in Healthcare

Patient engagement has been around since the days of Hippocrates. Everyone who has undergone medical treatment is familiar with the paper handouts and pamphlets so common in traditional patient engagement — experts say roughly eight of 10 hospitals still use paper handouts for patient education upon discharge. But there…


Natural Language Processing

Natural Language Processing
Natural Language Processing

Answering a specific, clearly defined research question from the massive amount of healthcare-related scholarly and clinical literature in existence can be extremely challenging. Yet that’s precisely the goal of systematic literature reviews (SLRs) in health care, which use a systematic approach to critically appraise and evaluate vast amounts of quantitative and qualitative data about a specific health-related issue.

SLRs provide an exhaustive summary — especially compared to other review types, such as rapid reviews — of all the evidence available on a specific research question, to make this evidence more readily available to key decision-makers. To have the most value…


Artificial Intelligence, Neuroscience

Biomedical engineer Chethan Pandarinath develops prosthetics — but not just any prosthetics. That’s because the Emory University and Georgia Tech researcher’s goal is to enable those with paralyzed limbs to use those arms as if they were their own, via signals from their brain.

Pandarinath hopes to achieve this by analyzing brain activity recordings of paralyzed people to identify neuronic patterns corresponding to specific movements. In theory, these patterns could power artificial intelligence (AI) systems connected to prosthetic limbs, enabling similar movement control over what’s essentially a foreign object attached to the body.

If that sounds complicated, it’s because it…


Pharmacovigilance — also known as PV, PhV, or drug safety — is a vital component of the drug development process, for protecting the health and safety of healthcare consumers and keeping drugmakers informed of any adverse drug reactions (ADRs) their products may cause in specific individuals.

PV involves “identifying, tracking, evaluating, and preventing negative outcomes” from drug therapies. It has seen “huge growth” over the past few years: According to European Pharmaceutical Manufacturer Magazine, the PV market will surpass US$8 billion by 2024. That’s because of the sheer number of drugs in development or on the market today, along with…


Artificial Intelligence

Data volumes and velocities in health care and other industries are growing at incredibly fast rates, driven largely by cloud computing and the proliferation of smart connected devices across the world. This information explosion has created opportunities for organizations to improve processes through analytics, but also significant challenges in terms of their capacity to handle all that information.

And while the big data revolution has touched virtually every business unit across the health care spectrum, nowhere has been more affected than the data center.

Indeed, this explosion in data generation has put tremendous pressure on data center infrastructure where tens…


Artificial Intelligence, Opinion, Technology

Reinforcement Learning
Reinforcement Learning

At a TED Talk back in 2010, game designer and author Jane McGonigal argued that video games would help change the world for the better. While she may not have been referring to health and wellness specifically, recent developments in reinforcement learning (RL) for health care have rapidly turned parts of McGonigal’s vision into reality.

In many ways, RL isn’t much different from other types of machine learning (ML), including deep learning or classic ML techniques. RL is simply a narrower subset of ML — “the cherry on the cake” of artificial intelligence (AI), according to Facebook VP and Chief…


Artificial Intelligence, Opinion

Explainable AI
Explainable AI

Picture this: You’re using an AI model when it recommends a course of action that doesn’t seem to make sense. However, because the model can’t explain itself, you’ve got no insight into the reasoning behind the recommendation. Your only options are to trust it or not — but without any context.

It’s a frustrating yet familiar experience for many who work with artificial intelligence (AI) systems, which in many cases function as so-called “black boxes” that sometimes can’t even be explained by their own creators. For some applications, black box-style AI systems are completely suitable (or even preferred by those…


Artificial Intelligence, Technology

Medical images such as computed tomography (CT), magnetic resonance imaging (MRI), mammograms, X-Ray types such as fluoroscopy and angiography, and ultrasounds are a valuable trove of potentially life-saving data for healthcare researchers, providers, and their patients.

But these kinds of images also present unique challenges that have traditionally limited radiologists’ effectiveness in diagnosing and treating various ailments. Consider that most medical image analysis today is performed by only a small group of (often overworked) radiologists or clinical doctors. That means the interpretations of these scans are, at best, extremely subjective — and, at worst, they can be entirely off base.


Ask most people to define natural language processing (NLP), and they’ll likely shrug their shoulders.

What they may not realize, however, is that much of their lives already revolve around this technology. From setting up email filters to asking Siri or Alexa which route to take or the ingredients of their favorite dish, to using search engines on the internet, NLP is everywhere in the consumer world. It’s become increasingly prevalent in the business world, too, through automated summarization, translation, sentiment analysis of media and other content, and other complex applications.

But the increasingly large demands placed on NLP, thanks…

Gaugarin Oliver

Chairman & CEO at CapeStart — www.capestart.com (A leading AI solutions provider — End-to-End Data Annotation, Machine Learning and Software Development)

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