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Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world. However, as longevity increases, healthcare systems face growing demands for their services, rising costs, and a workforce that is struggling to meet the needs of its patients. Healthcare is one of the most critical sectors in the broader landscape of big data because of its fundamental role in a productive, thriving society. Building on automation, Artificial Intelligence (AI) has the potential to revolutionize healthcare and help address some of the challenges set out above. The application of AI to healthcare data can literally be a matter of life and death. AI can assist doctors, nurses, and other healthcare workers in their daily work. AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient outcomes overall. This book gives insights into the latest developments of applications of AI in biomedicine, including disease diagnostics, pharmaceutical processing, patient care and monitoring, biomedical information, and biomedical research.
It also presents an outline of the recent breakthroughs in the application of Artificial Intelligence in healthcare, describes a roadmap to building effective, reliable, and safe AI systems, and discusses the possible future direction of AI augmented healthcare systems. AI has countless applications in healthcare. Whether it’s being used to discover links between genetic codes, to power surgical robots or even to maximize hospital efficiency AI has been a boon to the healthcare industry.
AI is no single technology however, it is a collection of various technologies whole together. Although most of the technologies have a significant and rel­evant role in the healthcare sector, the precise tasks and processes they sup­port vary in nature. Therefore, we are choosing the specific AI technologies that are of great importance to the healthcare domain. One type of AI is machine learning. Additionally, it is a statistical tactic to learn through exercising the data model, and the connection between AI, Deep Learning, and learning related to the machine is used to describe how computers learn. The author stated that they (AI, Deep Learning, neural networks, and machine learning) are similar to Russian nesting dolls as every component is essential to the preceding team. In the healthcare domain, precision medicine is the most collective presentation of outmoded machine learning. This consists of predicting the treatment protocols which are likely to succeed for the ill person grounded on the various disease attributes and the context of an intervention. However, for precise machine learning, one needs to take the training for calculating datasets and variable outcomes, and the process of learning is designated as supervised learning