Top 4 Ways Artificial Intelligence Is Transforming Healthcare

Source: quantumcomputingtech

According to Accenture analysis, when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026.

The ripe healthcare industry is ready to embrace some major changes. Right from chronic diseases, cancer, to risk assessment, early detection, there are a plethora of opportunities to leverage Artificial Intelligence technology. AI will enable more precise, efficient, and revolutionary interventions at the right moment in a patient’s care. 

Considering the current COVID-19 scenario the volume of data available in the field has surged to an all-time high and it is continuously increasing at a staggering rate, AI is poised to be the engine that drives enhancements across the care continuum. 

Growth in the AI health market is expected to reach $6.6 billion by 2021—that’s a compound annual growth rate of 40 percent. In just the next five years, the health AI market will grow more than 10×2.

While traditional analytics was immensely beneficial, AI offers a number of advantages over it. AI’s algorithms have the potential to become more precise and accurate as they interact with the training data. This will open the doors for humans to gain unprecedented insights into the world of diagnostics, treatment variability, and care processes. 

There are numerous ways in which AI can transform the Healthcare Industry, in this blog we will explore the best of them. Lets begin!

Computer Vision in Medical Imaging

Computer vision can be a highly effective way to improve medical analysis by extracting, analyzing and visualizing the structural and functional properties of biological tissues. 

This has perhaps been the reason why Medical Imaging via computer vision is the most widely referenced use case for AI in healthcare. Examining a medical scan to determine a skin lesion, a tumor or any other such indicators is exactly what computer vision and deep learning excel at. 

As AI legend Geoff Hinton famously declared in 2016, “People should stop training radiologists now. It’s just completely obvious that within 5 years, deep learning is going to do better than radiologists.”

AI in healthcare is still in its primitive stage, although many startups like Paige, Caption Health, PathAi have excelled in the field of medical imaging services the adoption still seems to be a big hurdle to overcome. Doctors are important but if the above statement by Geoff Hinton can come true then this is more of a threat than a solution for the medical fraternity. 

AI in Patient Care in Hospital 

For people with Alzheimer’s disease, AI has proven to be extremely helpful in helping them with their daily activities. There was a famous story of 59-year-old Brain Leblanc, who started using Alexa on his Amazon Echo Dot for reminders to do all the daily routine chores. 

Virtual Assistants can prove to be like actual assistants, they can not only give timely reminders but with time also learn about the typical routine of the individual and become just like a friendly companion in the long run. 

Besides this all hospitals house therapists, AI has made its way there too. Robotic-Assisted Therapy is gaining a lot of popularity. Bionik Laboratories in Toronto and Watertown, Mass., uses robotics and AI to help patients in their stroke recovery. 

It was noticed that patients ended up performing more recorded movements per hour than they could have with human assistance. 

Natural Language Processing (NLP) in Medical Documentation

“There’s this explosion of data in the healthcare space, and the industry needs to find the best ways to extract what’s relevant.”

NLP has made its presence felt very strongly in the area of medical documentation. Hospital Administrations have been immensely benefited by the efficiency it brings on board. 

  1. The Department of Veterans Affairs used NLP to review more than 2 billion EHR documents for indications of PTSD, depression, etc. The pilot was 80% accurate in determining the difference between records of screenings for suicide and mentions of actual past suicide attempts. 
  2. For deciphering the semantic meaning of specific clinical terms contained in free-text clinical notes, Researchers at MIT were able to attain a 75% accuracy. 
  3. UCLA researchers analyzed free text to mark patients with cirrhosis. By combining the NLP of radiology reports with ICD-9 codes and lab data, the algorithm attained incredibly high levels of sensitivity and specificity. 

With the fact that an on-duty physician spends 5.9 hours per day engaged with Electronic Health Records (EHR’s), a technology like NLP can be a boon to shorten this time. 

There are many such medical success stories with NLP and it has been adopted at a much faster rate than computer vision techniques. 

AI in Medical Devices 

Consumer environment is being dominated by ‘Smart Devices’. IoT has truly made tracking a patient’s health very easy for the medical community. In the medical environment monitoring is a very critical task whether its in the ICU or elsewhere. 

Using AI to enhance the ability to identify deterioration, suggests that sepsis is taking hold, or sense the development of compilations at regular intervals can make the diagnostics more effective. It can reduce the fatality and also costs incurred by the hospitals. 

“When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on – the aggregation of that data is not something that a human can do very well,” said Mark Michalski, MD, Executive Director of the MGH & BWH Center for Clinical Data Science. 

What Should You Prefer AI-Powered Smart Data Preparation in Building ML Models for Healthcare?

Every computer vision, deep learning, and NLP model needs labeled training data. To get highly accurate labeled data an AI-based data annotation platform is best preferred over a manual annotation. 

Manually annotating data can be a tedious task. Manual Data Labeling Challenges:

  • Managing and maintaining the quality of data labeling
  • Workforce management 
  • Keeping a track of the cost incurred
  • Compliance with data privacy requirements
  • The task to ensure data security

Read More on The Daunting Task of Manual Labeling in Retail and CPG and how Automation Helps

Labellerr – The Fastest and Smartest Data Annotation Platform

Labellerr provides you simple, feature-rich, affordable data annotation solution.

Why Choose Labellerr?

  1. Data Labelling at scale is an important concern for an organization since creating labels on large data sets by hand is often too slow and expensive. Labellerr solves this problem with their agile ML-Powered data annotation platform. 
  2. Work Quality and Worker productivity is difficult to track in the case of crowdsourcing and freelance data labeling services. Hence now with Labellerr’s marketplace, you can choose from our hand-picked and most trusted vendors to get data labeling tasks done.
  3. Domain and context capabilities specific to tasks are limited with workers on crowdsourcing platforms, contractors, and freelancers. 

So, if you wish your data annotation task to be automated and error-free then choose Labellerr.

Benefits of Labellerr’s Data Annotation Platform:

  • Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
  • Track work quality and worker productivity with a personalized dashboard experience.
  • Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores. 

The above is still just an indicative list, the superior benefit of adopting an automated way of labeling dataset is peace-of-mind and trust. The idea here is simple – let the machine do all the work for you so that your focus can be on your customers!

If you wish to try the power-packed machine learning embedded data annotation platform – Labellerr then just click here and explore.

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