Top 5 Jaw-Dropping Applications of Deep Learning in Healthcare Sectors


In this modern era of cutting-edge technology, healthcare organizations of all shapes and sizes are acquiring artificial intelligence and cognitive technology and are becoming increasingly interested in how these revolutionary technologies improve efficiencies and reduce costs.  

The sophistication and the availability of artificial intelligence have exploded over a short time span, leaving players, providers, and numerous other stakeholders with a dizzying array of technologies, tools as well as strategies to choose from. Only learning the lingo has been the biggest obstacle for numerous organizations. Organizations deal with different sorts of data each day and it is mandatory for them to integrate themselves with such innovative technologies that assist them in data analysis and to whip data of organization into shape. 


Healthcare sectors must feel confident that they have a strong grip on different flavors of artificial intelligence in order to choose vendor products or hire appropriate data scientist staff for the development of in-house algorithms accurately, efficiently, and effectively. The best place to start is deep learning, as this branch of artificial intelligence proves to be really transformative in the healthcare sector. It provides the facility of data analysis with a high rate of precision which is hard to ignore.

On the basis of deep learning models called artificial neural networks, each layer is assigned with a certain portion of a transformative task. Data traverses the layers numerous times to refine as well as optimize the ultimate output. Amid the coronavirus outbreak, the global deep learning industry struck the figure of US$4.4 billion during the year 2020. 

In the real-world clinical environment, deep learning is steadily finding its way into innovative technologies and tools. Here are the top five promising applications of deep learning for enhancing the health IT user experience.   

1. Drug Discovery and Manufacturing 

The early-stage drug recovery process is the most primary application of deep learning in the healthcare sector. This involves R&D technologies, including precision medicine and next-generation sequencing which assist in figuring out alternative paths for the therapy of multifaceted diseases.

Presently, deep learning techniques include unsupervised learning which identifies patterns in data without giving any sort of predictions. Project Hanover, developed and deployed by Microsoft, is utilizing deep learning-based technologies for numerous initiatives such as artificial intelligence-powered technology for the treatment of cancer and personalization of drug combinations for Acute Myeloid Leukemia (AML). 


2. Personalized Medicine 

Personalized treatments prove to be effective only when it is paired with current health conditions of individuals with predictive analytics. This also proves to be beneficial for future research and disease assessment.  Physicians are bound to choose from a specific set of diagnoses or provide risk estimation of patients on the basis of available genetic information or symptomatic history in this present age.

Fortunately, deep learning in medicine makes splendid strides. IBM Watson Oncology is immensely acquiring deep learning algorithms and utilizes patient’s history for the generation of numerous treatment options. More innovative biosensors and devices would be seen in the market in the upcoming years with sophisticated health measurement capabilities, permitting more data to be readily available for such revolutionary deep learning-based healthcare technologies. 


3. Smart Health Records 

One of the most crucial yet exhaustive processes in the healthcare sector is maintaining an up-to-date health record. Even though a majority of the technologies are playing their part to ease the data entry process, still a majority of the processes are time-consuming.

The main role of deep learning in healthcare is to enhance the data entry process and save time, money, and effort. Document Verification and categorization methods utilizing vector machines and deep learning-based OCR recognition techniques are gradually attaining stream, including machine learning-based handwriting recognition technology in MATLAB and Google’s Cloud Vision API.

Today, MIT is revolutionizing at a swift pace to develop the next generation of smart health records, which incorporates clinical treatment suggestions and more.


4. Outbreak Prediction

In this current era of cutting-edge technologies, artificial intelligence-powered and deep learning technologies for monitoring and predicting pandemics all across the globe. Scientists have access to an immense amount of data today which is collected via satellites, website information, real-time social media updates, and more.

Artificial neural networks play a promising role to analyse collected information and make predictions about everything, from malaria outbreaks to coronavirus pandemics to severe chronic infectious diseases. Prediction of such outbreaks is very crucial especially in this present age when the complete world lacks medical infrastructures as well as educational institutions.

ProMED-mail which is basically a reporting platform based on the internet is an essential example of this. This platform detects emerging diseases and provides a real-time report of those diseases. 

5. Better Radiotherapy  

The radiology field is one of the most sought-after applications of deep learning in the healthcare sector. Medical image analysis has numerous discrete variables which can evolve at any certain moment of time. There are numerous cancer foci, lesions, etc which cannot be moduled only by utilizing complex equations.

It becomes easier to diagnose and find variables since deep learning-based algorithms learn from the multitude of different available samples. Categorization of objects is one of the most sought-after use cases of deep learning in medical image analysis.


Google’s DeepMind Health is actively assisting researchers in UCLH to develop algorithms for the detection and differentiation of cancerous and healthy tissues for the enhancement of radiation treatment. 

Future Implications 

The acquisition of deep learning algorithms is progressing tremendously by leaps and bounds in the healthcare sector. According to researchers and analysts, the global market of deep learning will reach $44.3 billion by the year 2027. Providers have been encouraged by ethics researchers such as Drs. Magnus and Char for the further incorporation of artificial intelligence and deep learning algorithms to treat patients. The major goal of ethics researchers is to ensure patient’s autonomy. 

With the progressing efficiency of deep learning predictive algorithms and the increasing reliance on artificial intelligence systems for diagnostic aid, the legitimacy of artificial intelligence and deep learning in healthcare is still a major topic for debate. Fear resides in the complete insulation of physicians by these magic algorithms, but that’s not impending. However, deep-learning-based systems are encouraged to be utilized with inspection and scrutiny.


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