How Is the Medical Industry Using Deep Learning?

How Is the Medical Industry Using Deep Learning?

The healthcare industry continues to be a major driver of the U.S. economy, with more than $3.5 trillion spent on healthcare in 2018 alone. Researchers believe that the industry will contribute more than $5.6 trillion to the economy by 2025 as well. 

Much of this revenue comes from the medical research field, which is responsible for improving drug research, disease diagnosis and treatment protocols. Major research companies are collaborating with software development services to integrate deep learning technology into their investigations. 

Deep learning promises to transform the way that doctors review medical tests and make diagnoses, helping them identify diseases and start treatment quicker. The technology will also help pharmaceutical companies develop life-saving drugs in a shorter amount of time. 

What is deep learning?

Deep learning is a niche in the machine learning field. Machine learning is an innovative branch of computer science that combines artificial intelligence (AI) and traditional science with mathematics and statistics to create a powerful technology that can learn from experience.

Major companies in the retail, banking and logistics industries are already working with software development companies and using the technology to save money, increase efficiency and plan for the future.

Machine learning is premised on the idea that computer engineers should be able to do more than just code a program – they should be able to teach computers how to write their own programs. Additionally, these “intelligent” computers should be capable of learning from past experiences to improve their skills and the quality of their predictions.

Deep learning takes things a step further by mimicking the structure of the human brain, which is layered, or “deep.” It emulates this layered structure by creating a similar, artificial neural network with the potential to be even more powerful than cutting-edge machine learning software.

For example, the transportation industry is using deep learning to teach computers how to recognize images. The autonomous car startup Wayve.ai taught its cars how to quickly and accurately recognize road conditions by exposing the system to millions of images of roadways. Medical companies are using the same tactics to transform their own industry.

How deep learning is transforming medical research

This type of active learning is vital for the medical research industry. That’s because computers can learn from new medical research and past performance data to improve the quality and accuracy of diagnoses.

Deep learning will transform the medical research industry by introducing more advanced data analysis into research efforts and by eliminating the need for a human expert with years of training.

This cutting-edge technology is also expected to reduce the time needed to make a diagnosis. Rather than spending several months or more going from specialist to specialist, patients will soon work with their physician to enter their symptoms and test results into a computer. 

The deep learning program will use data points from millions of other patients to produce accurate diagnosis months or even years before a human doctor could. This will help reduce patient anxiety and improve healthcare outcomes through early treatments.

Pharmaceutical discovery

The pharmaceutical industry is rapidly becoming a leader in deep learning adoption. Major companies, like Amgen, AstraZeneca, Bayer, Eli Lilly and more are building up their internal research teams and collaborating with software development providers to increase efficiency in their drug discovery processes. 

A major reason why deep learning is becoming so popular in the medical research industry is because of the massive amount of data that these companies possess, including research data, patient outcomes and more. This massive amount of information is key to producing accurate predictions, the biggest reason why the pharmaceutical industry is investing so much in deep learning.

BenevolentAI is an excellent example of this. The company has created a set of custom algorithms that search current and past medical research for clues about potential new drugs. In particular, the software uses the data to identify specific classes of drugs that may be useful for the treatment of intractable diseases. 

On the other hand, companies like Sophia Genetics are using deep learning to inform patients about their susceptibility to hereditary diseases, using specific genetic markers associated with cancer, heart disease, diabetes and more. 

Medical imaging

One of the biggest challenges that medical specialists face is accurately reading and understanding medical imaging. This is incredibly important since medical imaging is one of the key factors used to build a medical diagnosis, along with symptoms and blood tests.

For example, oncologists still struggle to identify cancerous cells versus non-cancerous cells when viewing MRIs and CT Scans. They look incredibly similar and a misread image can cause a late diagnosis with a corresponding bleak survival rate. That’s one reason why cell biopsies continue to be so important to the field. 

However, medical research companies are changing this paradigm through deep learning. By exposing machine learning software to millions of stored images, along with the corresponding disease diagnosis, they are teaching these machines how to more accurately read medical images. 

IBM Watson is one of the leaders in the deep learning medical image field. The tech company’s machine learning software is superior to many competitors because of the huge amount of data owned by IBM. The company has spent the past decade purchasing healthcare companies like Merge. As a result, it now has access to more than 315 healthcare data points.

Expect IBM to apply for FDA approval for its technology sometime in the next five years. It’s clear from IBM public statements that the company is waiting to seek approval until its technology can do more than basic medical imaging.

Disease identification

One of the most important areas of deep learning research is disease identification. Research has found that at least 5% of medical diagnoses in any given year are incorrect. This impacts an estimated 12 million patients every year, resulting in 40,000 to 80,000 deaths. 

Medical research companies are using deep learning and AI to improve disease identification accuracy. The healthcare startup iQuity is a great example of the potential of deep learning. The company is using deep neural networks to improve patient outcomes and disease detection. It recently used its cutting-edge technology to improve multiple sclerosis diagnoses.

It started by collecting millions of data points from multiple sclerosis-related insurance claims in New York state. Its deep learning program was eventually able to accurately diagnosis the disease “at least 8 months” before physicians could make that assessment using standard medical technology. 

Deep learning promises to transform the way that healthcare providers diagnosis diseases and improve survival rates and quality of life by allowing doctors to treat diseases before irreversible damage has happened.

Looking forward

The global healthcare industry is expected to continue growing by leaps and bounds over the next decade. This is largely a result of increased access to healthcare in developing countries, the rise of lifestyle diseases throughout the world, and the development of new, novel drugs that can be sold at premium rates. 

Deep learning promises to revolutionize the medical research industry in several ways. It will help physicians better read imaging results and improve the accuracy of their diagnoses. In addition, pharmaceutical research companies will be able to produce more drugs with less monetary investment through machine learning.

Expect to see the number of deep learning medical patents increase dramatically as the industry’s investments in machine learning and software development outsourcing begin to pay off.

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