Healthcare industry is deeply interested in utilizing the power of artificial intelligence to provide better treatment and patient care at the same time improving their efficiency and lowering the cost of medication and medical facilities.
Over the past few years, AI has exploded into many fields and sub-fields with multiple technologies and sub-tools used under the AI development process. Machine learning, deep learning and semantic computing are the few terms making significant differences between them that help to develop a functional AI model.
To develop such models high-quality data is ingested, analyzed and returned to end-users that have a big impact while expecting the results with complete reliability and accuracy. In order to get choose between the right data scientists and suitable algorithms, healthcare organizations need to stay confident to adopt the different flavors of artificial intelligence and how they can apply this technology into different use cases.
Deep learning is the right place to start working on AI development process. And over the past few years this branch of AI has become transformative for healthcare providing better insights to analyze the data with better speed and accuracy.
But here you need to understand about deep learning and how it is different from machine learning or how the healthcare industry are leveraging deep learning techniques to solve the crucial problems while providing the patient care.
Deep Learning is a kind of machine learning process that uses a layered algorithmic architecture to analyze the data and make it easier to train the machines with suitable data sets and make a fully functional AI model.
Under the deep learning process, data is filtered through multiple layers and with a successive layer using the results from the previous one to tell its output. Deep learning based models can come more and more accurate as they process more data, mainly learnt from previous outputs to refine their ability to make a better connection and correlations.
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Currently, deep learning is mainly adopted for small scale model testing or at research projects and at pre-commercialized stages. However, on the other hand deep learning is also gradually find new ways into various innovative tools that have high-value applications in the healthcare industry. While few of the promising cases includes innovative applications for patients, and few of them surprisingly establish strategies for improving the end-users experience through IT related services in healthcare.
Conventional neural network (CNNs) is one the type of deep learning that is used for analyzing the images like X-rays, CT Scan and MRI to diagnosis the possible diseases. This neural network technology is designed with a motive to that it will process images, allowing the networks to handle the larger images and operate with better efficiency.
Several schools of medicine and research centers have developed a deep neural network capable of diagnosing the crucial neurological conditions, such as stroke and brain hemorrhage, around a hundred times quicker than human radiologists with the highest accuracy and some CNNs are even surpassing the accuracy of human diagnosticians.
Deep learning and neural network, numerous natural language processing tools have become prevalent in the healthcare industry for translating the speech into text or converting them into dictation documents with accurateness.
Actually, neural networks have been designed for classification and they can easily identify individual linguistic or grammatical elements by “grouping” similar words together and mapping them in relation to one another. And this helps the network understand the complex semantic meaning but such tasks are affected due to distinctions of common speech and communication.
Though, most of the deep learning tools still find difficult while identifying the important clinical elements fail to establish a meaningful relationship between them, and translate such relationships into useful information for an end user.
Deep learning also works with precision medicine and drug discovery agenda to provide the most advance medical research systems to developers. And to achieve such an agenda a huge volume of genomic, clinical, population-level and healthcare training data with the goal of identifying previously unknown associations between genes, pharmaceuticals, and physical environments is required with the right algorithm.
The medical research centers and universities are seriously working with deep learning technologies that will help to accelerate the process of analyzing data, these institutions also expecting the combination of predictive analytics and molecular modeling will hopefully uncover new insights into how and why certain cancers form in certain patients that will help to provide better and timely cure and treatments.
The role of deep learning in clinical analysis for predictive decisions is getting stronger with the hope of analyzing the variety of health conditions of different patients. In several conditions, deep learning may soon be a handy diagnostic tool in the inpatient setting, where it can alert attendants to changes in high-risk medical care conditions.
Certain artificial intelligence in healthcare laboratory and computer science centers have developed a project called ICU intervenes that enables to inform the clinicians to the patient under the critical care unit. In few eye related clinical trials, professionals use optical coherence tomography (OCT) scans to diagnose eye conditions.
And these 3D images provide a detailed map of the back of the eye, but they are hard to read and need expert analysis to interpret. But with the help of image annotation deep learning, the system an accurate as a human clinician, and has the potential to significantly expand access to care by reducing the time it takes for testing and diagnosis.
Deep learning is showing progressive growth with prevalent opportunities in the healthcare sector to develop more useful and efficient applications or computer systems that can provide better information with more quick and accurate results.
Further with the use of AI in the healthcare setting, some deep learning algorithms will produce “transformational” outcomes with high accuracy. The use of deep learning and NLP Sentiment Analysis with the right algorithm will help to understand casual conversation in a noisy environment, giving rise to the possibility of using a comprehensive, intelligent scribe to support the burden of documentation.
All these initiatives and developments will not only help return joy to practice by facilitating doctors and reduce their everyday workload but at the same time also help the patients get more dedicated and thorough medical attention, ideally, leading to better care and quick recovery from critical diseases with least agony.