Recent Trends of Implementations of Deep Learning in Healthcare
Article contributed by Mr. Debashis Ghosh, Associate Professor, Amrapali Institute of Technology and Sciences
In the present time Artificial Intelligence(AI), Machine Learning(ML) and Deep Learning(DL) have gained a huge popularity and acceptance in almost all fields of our society. Currently the situation has changed more speedly with the outbreak of Covid-19 pandemic. During the present crisis we are observing a rapid digital transformation and the adaptation of disrupted technology across the universe. Healthcare sector has gained many benifits from the implementation of disruptive technologies.
Artificial Intelligence, Machine Learning and Deep Learning are taking a very important role for the very high level improvement of the sector. DL in the healthcare sector has the ability to analyze the medical data at exceptional speeds without compromising on it’s accuracy. So plentiful – fast, efficient, accurate are the key benefits of the DL in Healthcare sector but benefits are not limited to these only, it has lots more. DL uses mathematical and statistical models that are designed to operate almost like a human brain.
In the healthcare sector Deep Learning is successfully used in medical imaging solutions, for identifying patterns in patient symptoms, DL algorithms can identify specific types of cancer, and DL imaging solutions can be used to identify rare diseases. Deep learning takes an important role for the medical professionals to identify treatment issues early, so they are able to deliver far more personalized and relevant patient care. Here are some very important applications of Deep Learning in the healthcare sector.
Medical imaging and diagnostics: Medical images like X-ray, MRI scan, CT scan, etc can be interpreted by Deep learning models to perform diagnosis. Recently invented computer vision is enabled by machine learning and deep learning so it is possible to do faster and easier diagnosis of diseases through medical imaging.
Personalized treatment: Deep learning models easily analyze a patient’s health data, his medical history, vital symptoms, medical test results, and so on. ML models can use deep neural networks to analyze real-time data collected from connected devices and predict the patient’s upcoming health conditions or risks and provide him specific medicines or treatments.
Patient monitoring and smart health record:Deep learning can be used to classify the structured and unstructured medical data and maintain smart health records. DL can help in intelligently monitoring the patients and predict their risks by analyzing the lots of real-time data collected from the healthcare devices.
Simplification of Clinical Trials: Machine learning and deep learning models can be used for predictive analysis for identifying potential candidates for clinical trials from different data points and sources. Deep learning is also able to continuous monitoring of these trials with minimum errors.
Drug Discovery: Deep Learning has the significant role for identifying drug combinations. Disruptive technologies like AI, ML, and DL play very important roles in the pandemic like situation for vaccine and drug development. Drug discovery is a very complex and time consuming task, but deep learning can make it easier, faster, cost-effective r.
Health insurance and fraud detection: Health insurance providers are not lagging to advantages of Deep Learning. DL models can help them to predict the future trends and behavior for suggesting smart insurance policies to their clients. DL can be used to effectively identify insurance frauds and predict future risks.
Implementation of Deep Learning for various healthcare applications are discussed so far in this blog . But all of the cases Deep Learning is still in early stages. So researchers can find out their research topics for implementing Deep Learning for further improvement.