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Define Artificial intelligence and explain that role it could have in healthcare in the future

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Define Artificial intelligence and explain that role it couldhave in healthcare in the future

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Artificial intelligence AI in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data The primary aim of healthrelated AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes ABSTRACT The complexity and rise of data in healthcare means that artificial intelligence AI will increasingly be applied within the field Several types of AI are already being employed by payers and providers of care and life sciences companies The key categories of applications involve diagnosis and treatment recommendations patient engagement and adherence and administrative activities Although there are many instances in which AI can perform healthcare tasks as well or better than humans implementation factors will prevent largescale automation of healthcare professional jobs for a considerable period Ethical issues in the application of AI to healthcare are also discussed KEYWORDS Artificial intelligence clinical decision support electronic health record systems Introduction Artificial intelligence AI and related technologies are increasingly prevalent in business and society and are beginning to be applied to healthcare These technologies have the potential to transform many aspects of patient care as well as administrative processes within provider payer and pharmaceutical organisations There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks such as diagnosing disease Today algorithms are already outperforming radiologists at spotting malignant tumours and guiding researchers in how to construct cohorts for costly clinical trials However for a variety of reasons we believe that it will be many years before AI replaces humans for broad medical process domains In this article we describe both the potential that AI offers to automate aspects of care and some of the barriers to rapid implementation of AI in healthcare Types of AI of relevance to healthcare Artificial intelligence is not one technology but rather a collection of them Most of these technologies have immediate relevance to the healthcare field but the specific processes and tasks they support vary widely Some particular AI technologies of high importance to healthcare are defined and described below Machine learning neural networks and deep learning Machine learning is a statistical technique for fitting models to data and to learn by training models with data Machine learning is one of the most common forms of AI in a 2018 Deloitte survey of 1100 US managers whose organisations were already pursuing AI 63 of companies surveyed were employing machine learning in their businesses1 It is a broad technique at the core of many approaches to AI and there are many versions of it In healthcare the most common application of traditional machine learning is precision medicine predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context2 The great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable eg onset of disease is known this is called supervised learning A more complex form of machine learning is the neural network a technology that has been available since the 1960s has been well established in healthcare research for several decades3 and has been used for categorisation applications like determining whether a patient will acquire a particular disease It views problems in terms of inputs outputs and weights of variables or features that associate inputs with outputs It has been likened to the way that neurons process signals but the analogy to the brains function is relatively weak The most complex forms of machine learning involve deep learning or neural network models with many levels of features or variables that predict outcomes There may be thousands of hidden features in such models which are uncovered by the faster processing of todays graphics processing units and cloud architectures A common application of deep learning in healthcare is recognition of potentially cancerous lesions in radiology images4 Deep learning is increasingly being applied to radiomics or the detection of clinically relevant features in imaging data beyond what can be perceived by the human eye5 Both radiomics and deep learning are most commonly found in oncologyoriented image analysis Their combination appears to promise greater accuracy in diagnosis than the previous generation of automated tools for image analysis known as computeraided detection or CAD Deep learning is also increasingly used for speech recognition and as such is a form of natural language processing NLP described below Unlike earlier forms of statistical analysis each feature in a deep learning model typically has little meaning to a human observer As a result the explanation of the models outcomes may be very difficult or impossible to interpret    See Answer
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