Abstract
Artificial intelligence (AI) can now be very popular in various healthcare sectors. It deals with both structured and unstructured medical data. Common AI techniques in which machine learning procedures are used for structured data are neural network and the classical support vector machine (SVM) as well as natural language processing and modern deep learning for unstructured data. The main disease areas where AI tools have been used are cancer, cardiology, and neurology. The development of pharmaceuticals via clinical trials can take more time even decades and very costly. Therefore, making the process quicker and inexpensive is the main objective of AI start-ups. AI thus has a wide application in the field of biomedical engineering. AI can also help in carrying out repetitive tasks, which are time-consuming processes. Tasks such as computed tomography (CT) scans, X-ray scans, analyzing different tests, data entry, etc. can be done faster and more precisely by robots. Cardiology and radiology and 126are two such areas where analyzing the amount of data can be time-consuming and overwhelming. In fact, AI will transform healthcare in the near future. There are various health-related apps in a phone that use AI like Google Assistant, but there are also some apps like Ada Health Companion that uses AI to learn by asking smart questions to help people feel better, takes control of their health, and predicts diseases based on symptoms. As in expert systems, AI acts as an expert in a computer system that emulates the decision-making ability of a human expert. Expert systems like MYCIN for bacterial diseases and CaDET for cancer detection are widely used. In image processing, it is very critical when it comes to healthcare because we have to detect disease based on the images from X-ray, MRI, and CT scans so an AI system that detects those minute tumor cells is really handy in early detection of diseases. One of the biggest achievements is a surgical robot as it is the most interesting and definitely a revolutionary invention and can change surgery completely. However, before AI systems can be arranged in healthcare applications, they need to be “trained” through data that are generated from clinical activities, such as screening, diagnosis, treatment assignment, and so on, so that they can learn similar groups of subjects, associations between subject features, and outcomes of interest. These clinical data often exist in but not limited to the form of demographics, medical notes, electronic recordings from medical devices, physical examinations, and clinical laboratories. AI has been intended to analyze medical reports and prescriptions from a patient’s file, medical expertise, as well as external research to assist in selecting the right, separately customized treatment pathway. Nuance Communications provides a virtual assistant solution that enhances interactions between clinicians and patients, overall improving patient experience and reducing physician stress. The platform enables conversational dialogue and prebuilt capabilities that automate clinical workflows. The healthcare virtual assistant employs voice recognition, electronic health record integrations, strategic health IT relationships, voice biometrics, and text-to-speech and prototype smart speakers customized for a secure platform. IBM Medical Sieve is an ambitious long-term exploratory project that plans to build a next-generation “cognitive assistant” that is capable of analytics and reasoning with a vast range of clinical knowledge. Medical Sieve can help in taking clinical decisions regarding cardiology and radiology—a “cognitive health assistant” in other terms. It can analyze the radiology images to detect problems reliably and speedily.
Published Version
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