Deep learning is redefining healthcare through its immense capabilities

AI and machine learning have gained a lot of popularity and acceptance in recent years. With the onset of the Covid-19 pandemic, the situation changed even more. During the crisis, we witnessed a rapid digital transformation and the adoption of disruptive technology across different industries. Healthcare was one of the potential sectors that gained many benefits from deploying disruptive technologies. AI, machine learning, and deep learning have become an imperative part of the sector. Deep learning in healthcare has a huge impact and it has enabled the sector to improve patient monitoring and diagnostics. Here are the top pathbreaking applications of deep learning in healthcare.

• Drug Discovery

The role of deep learning in identifying drug combinations is significant. During the pandemic, vaccine and drug development were funded by disruptive technologies like AI, machine learning, and deep learning. Since drug discovery is a complex task, deep learning can make it faster, cost-effective, and easier. Deep learning algorithms can predict the drug properties, drug-target interaction prediction, and in generating a compound with desired properties. Deep learning algorithms can easily process genomic, clinical, and population data and various toolkits can be used to detect patterns between the data. By leveraging machine learning and deep learning, researchers are now able to perform faster molecular modeling and predictive analytics in defining protein structures.

• Medical Imaging and Diagnostics

Deep learning models can interpret medical images like X-ray, MRI scan, CT scan, etc., to perform diagnosis. The algorithms can detect any risk and flag anomalies in the medical images. Deep learning is extensively used in detecting cancer. The recent innovation of computer vision was enabled by machine learning and deep learning. With a faster diagnosis through medical imaging, it becomes easier to treat diseases.

• Simplifying Clinical Trials

Clinical trials are complicated and expensive. Machine learning and deep learning can be leveraged to perform predictive analytics to identify potential candidates for clinical trials and enable scientists to pool in people from different data points and sources. Deep learning will also enable continuous monitoring of these trials with minimum errors and human intervention.

• Personalized Treatment

With deep learning models, it becomes easier to analyze patient's health data, medical history, vital symptoms, medical test results, and others. Hence, this enables healthcare providers to understand each patient and provide personalized treatment for them. These disruptive technologies enable the detection of suitable and multiple treatment options for different patients. With real-time data collection through connected devices, machine learning models can use deep neural networks to predict upcoming health conditions or risks and provide specific medicines or treatments.

• Improved Health Records and Patient Monitoring

Deep learning and machine learning models can process and analyze various medical and healthcare data, both structured and unstructured. Document classification and maintaining up-to-date health records might become manually difficult. Thus, machine learning and its subset deep learning can be used to maintain smart health records. With the advent of telemedicine, wearables, and remote patient monitoring, there is now abundant real-time data on health and deep learning can help in intelligently monitoring the patients and predict risks.

• Health Insurance and Fraud Detection

Deep learning can efficiently identify insurance frauds and predict future risks. Health insurance providers are also an advantage if they use deep learning because the models can predict the future trends and behavior to suggest smart insurance policies to their clients.

• Deep Learning and NLP

Natural language processing (NLP) leverages deep learning algorithms for classification and identification. These two technologies can be used in identifying and classifying health data and can also be leveraged to develop chatbots and voice bots. In the current scenario of telehealth, chatbots play a pivotal role. It makes the interaction with patients easier and faster. These chatbots were also used to spread the word about Covid-19 and answer primary queries.