Abstract
In this chapter, the authors will look at how AI is advancing healthcare in terms of diagnosis, prognosis, treatment, medical education and training of the students, primary care, and the challenges of using AI in the healthcare sector. Accurate diagnosis is the basis of the healthcare system worldwide. AI and machine learning are very powerful tools for assisting diagnosis and also predicting the future health of patients. With the recent advancements in the field of robotics, medical treatment has seen various health-related robotics applications. Surgeries and other medical practices can be done with the help of robots which have tremendous advantages like precision, no issue of fatigue in lengthy surgeries or following the procedure, etc. There are definitely challenges of using AI in healthcare, but with the ongoing research and the speed at which AI technologies are being improved, these challenges will be overcome sooner than we know.
TopIntroduction
Artificial Intelligence is a budding field in computer science that is applied to computational advancements that impersonate systems helped by human knowledge. These innovations have an interdisciplinary methodology and can be applied in different fields, similar to medication and medical care. Since the mid-1950s, AI has been incorporated into medication, when doctors endeavored to further develop their analyses utilizing computer helped programs. Over the years we have seen great advances in medical AI applications and healthcare applications because of the substantial enhancement in the computing powers of modern-day computers and the massive amount of medical data that is available digitally in the form of EHR. AI is gradually changing medical practice for the better. The amount of AI applications that are available in the industry today is overwhelming. These fields include diagnosis, prognosis, treatment, clinical, rehabilitation, surgery and predictive practices. Another very important area in which AI can demonstrate to ponder is clinical direction and infection conclusion. AI models can take in, analyze and report a large volume of data to diagnose diseases and guide medical decisions. AI technologies can deal with a large amount of medical data and are capable of finding new information based on that information that would otherwise remain hidden in the mass of medical data. This can also be used to identify and create new drugs for health services and patient care and treatment.
In healthcare, artificial intelligence is assisting physicians in producing better patient care, and administrative processes, improvising currently existing solutions and overcoming upcoming challenges faster. AI is not one technology, but rather a collection of them. For example, machine learning, deep learning, neural networks, natural language processing and the various algorithms that are curated. All these technologies have immediate relevance to healthcare and patient care, and the fact that all these technologies have varied concepts makes them more and more favourable to be used in medicine. As of right now, AI systems are outperforming radiologists in identifying dangerous malignancies and guiding analysts on how to create shorts for pricey clinical preliminary exams. However, even with the enormous progress we have made to far, it's unlikely that AI will completely replace humans for routine medical procedures and surgeries for several decades.
This chapter is structured to cover some aspects of healthcare. Section 1 talks about AI for Medical Education and Training, and the various practices that are used to train medical professionals. Section 2 deals with AI for Diagnosis, Section 3 with AI for Prognosis and Section 4 with AI and Treatment. These 3 sections talk about various algorithms and their accuracies in particular fields. AI for Medical Prognosis contains Virtual chatbot nursing assistants and is discussed in depth. Moving on to Section 5, AI for Fitness and Primary care and Section 6, Electronic health records(EHRs). Finally, Section 7 covers the challenges of Artificial Intelligence in Healthcare in-depth, with the subtopics being Errors and Discrepancies, Lack of accurate Medical Data, Security of Massive Medical Data and Bias and inconsistency.
Key Terms in this Chapter
Natural Language Processing: Branch of artificial intelligence that enables machines to understand human language, understand written text and also spell check them and translate one language to other language.
Support Vector Machine: It is a type of machine learning algorithm that analyses and classifies the data using a hyperplane.
Logistic Regression: It is an algorithm used in the software to understand the relation between dependent and independent variables by estimating probabilities using logistic regression equation. This algorithm helps you to predict the likeliness on an event happening.
Neural Networks: It is a bunch of connected nodes called neurons, which mimic the model of neurons like a biological brain.
Reinforcement Learning: It is a machine learning algorithm that deals with how the intelligence agent takes action in an environment in order to maximize result.
Machine Learning: It is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Supervised Learning: It is a machine learning algorithm that maps the input to the output based on a function.
Deep Learning: It is a branch of machine learning which helps the computer to learn how to do activities like humans do.
Unsupervised Learning: It is a machine learning algorithm that is used to make the computer learn from unlabelled data.