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Research Topics in Deep Learning for HealthCare

Research Topics in Deep Learning for HealthCare

   Deep learning owns the computing capability that provides accurate, fast, and efficient operations of medical data in healthcare. Deep learning plays a fundamental role in healthcare systems that identify patterns of diseased or abnormal conditions that develop in the human body, a lot faster than a clinician Medical imaging. The advantages of deep learning in healthcare are quick diagnosis, providing clinicians more time for patient care, and reducing errors in diagnosis by analyzing the prescriptions and diagnosing results in the healthcare industry better. Deep learning in healthcare assists the doctors, or medical professionals analyze any disease accurately and helps them to improve the treatment, thus resulting in superior medical decisions.

   Deep learning models can make predictions around hospitalized patients, supporting clinicians in managing patient data and outcomes. Many deep learning algorithms used in healthcare include Artificial Neural Networks, Convolutional Neural Network, Recurrent Neural Network, and LSTM. The application areas of deep learning in healthcare are Medical imaging, Healthcare data analytics, Mental health chatbots, Personalized medical treatments, Drug Discovery, Genomics, simplifying clinical trials, Fraud detection, and many more. Recent advances of deep learning in healthcare are Electronic health records with predictive modeling data, IoT in healthcare systems, multigrade brain tumor classification in the smart healthcare system, clinical decision support, early detection of covid-12, and analyzing of chest x-rays, among others.

   • In reality, medical data are spotty, high-dimensional, and heterogeneous nature which remains a key challenge in obtaining knowledge and actionable insights in the health care domain.

   • Deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data in healthcare.

   • Due to its efficiency and similarity, deep learning methods have become a preferred method of analyzing and processing medical data in healthcare.

   • Deep learning based on neural networks can automatically learn more abstract and generalized data features and provide end-to-end learning with integrated feature learning, the capability of handling complex and multi-modality data.

   • Even though deep learning has deserved increased attention in the medical health field and has a broad prospect of application, there remain some problems and challenges of deep learning in computational medical health involving insufficient data, interpretability, data privacy, and heterogeneity.

   • In recent years, healthcare to benefit immensely from deep learning because of the large volume of biomedical data being generated and the increasing proliferation of medical devices and digital record systems.