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2023 | Buch

Introduction to Human and Artificial Intelligence Kapitel lesen Erstes Kapitel lesen

Artificial Intelligence in Healthcare Industry

verfasst von: Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman

Verlag: Springer Nature Singapore

Buchreihe : Advanced Technologies and Societal Change

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book presents a systematic evolution of artificial intelligence (AI), its applications, challenges and solutions in the field of healthcare. The book mainly covers the foundations and various methods of learning in artificial intelligence with its application in healthcare industry. This book provides a comprehensive introduction to data analysis using AI as a tool in the generation, normalization and analysis of healthcare data in association with several evaluation techniques and accuracy measurements. The book is divided into three major sections describing the basic foundations of AI and its associated algorithms, history of artificial intelligence in healthcare, recent developments and several modeling techniques for the same. The last section of the book provides insights into several implementations and methods of evaluation and accuracy prediction for healthcare analysis in AI. Extensive use of data for analysis and prediction using several technologies has transformed the lives of normal people indirectly effecting our process to communicate, learn, work and socialize within the society. Thus, the book also provides an insight into the ethics of AI that is very vital in the process of implementation and evaluation of healthcare data. The book provides an organized analysis to a considerable part of data in a digitized society. In view of this, it covers the theory, methodology, perfection and verification of empirical work for health-related data processing. Particular attention is devoted to in-depth experiments and applications.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction to Human and Artificial Intelligence
Abstract
Intelligence can be described in education as the ability to learn or understand new challenges that may arise in any situation, or even deal with them. Broadly speaking, intelligence in psychological terms refers to a person's ability to apply knowledge to manipulate the environment or to think abstractly.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 2. Knowledge Representation and Reasoning
Abstract
Humans are the most adapt at comprehending, making sense of, and applying knowledge. Man has knowledge, or understanding, of some things, and he acts in the real world in diverse ways in accordance with this understanding.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 3. Methods of Machine Learning
Abstract
Machine learning is a kind of artificial intelligence that allows computers to teach themselves new skills and improve their performance based on what they've seen before. Machine learning is a collection of algorithms designed to process massive amounts of information. Algorithms are trained using data, and then use that training to create a model and carry out a task.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 4. Supervised Learning
Abstract
The fields of machine learning and artificial intelligence include the subfield of supervised learning, commonly known as supervised machine learning. It is characterised by the training of classification or prediction algorithms using labelled datasets. As more and more data is entered into the model, the model’s weights are adjusted through a process called cross validation until the data fits the model well. Organizations can use supervised learning to find large-scale solutions to a wide range of real-world challenges, including spam classification and removal from inboxes.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 5. Unsupervised Learning
Abstract
Unsupervised learning is a form of machine learning in which models are not guided by a specific training dataset. Instead, models unearth previously unseen correlations and insights within the data.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 6. Time-Series Analysis
Abstract
Despite the rise of other, more cutting-edge approaches to data analysis (machine learning, the Internet of things, etc.), time-series analysis remains a prominent statistical tool. Simply said, a time series is a set of time-related data points that are organised in a particular sequence. These checks are often performed at set time intervals, like once per second, minute, or hour. It can also be done on a monthly, yearly, or even a daily basis.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 7. Artificial Intelligence in Healthcare
Abstract
Healthcare organisations are beginning to adopt AI and related technologies as they become increasingly common in the business world. The implementation of these technologies has the potential to improve numerous facets of patient care and administrative processes within provider, payer, and pharmaceutical organisations.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 8. Rule-Based Expert Systems
Abstract
A rule-based expert system is the most elementary form of AI, and it uses predetermined sets of steps to find an answer to a problem. The goal of an expert system is to encode the knowledge of a human expert as a set of rules that can be applied to data automatically.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 9. Robotic Process Automation: A Path to Intelligent Healthcare
Abstract
Consider all the information and data that healthcare businesses process every day. Information from scheduling applications, HR applications, ERPs, radiology information systems, insurance portals, lab information systems, and third-party portals are all included. And it's a complex and time-consuming task to integrate the flow of information across all these channels. To make matters worse, most healthcare organisations still rely on human intelligence to do this time-consuming and error-prone work. The healthcare business stands to benefit greatly from robotic process automation, an efficiency driver.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 10. Tools and Technologies for Implementing AI Approaches in Healthcare
Abstract
The healthcare industry presents a unique set of challenges that can make data management a difficult process. Many parts make up this whole, such as management, coordination, storage, and, of course, evaluation. New healthcare business analytics instruments and software are released annually by technology firms in an effort to better the quality of care provided.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 11. Learning Evaluation for Intelligence
Abstract
The field of machine learning is only getting started. Only in the last quarter of a century have researchers focused on more advanced forms of machine learning, and this has led to a rise in the popularity of data science. And so, the data science community continues to marvel at the boundless possibilities of AI and machine learning. The industry as a whole is learning, growing, and facing new challenges as a result of this, which is both exciting and confusing.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Chapter 12. Ethics of Intelligence
Abstract
Ethical and moral concerns are prompted by the prospect of machine learning yielding creative answers to problems. Currently, governance is developing at the same rapid pace as the business world. There are many potential applications of AI for which neither a precedent nor any applicable laws or regulations exist.
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
Metadaten
Titel
Artificial Intelligence in Healthcare Industry
verfasst von
Jyotismita Talukdar
Thipendra P. Singh
Basanta Barman
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-9931-57-6
Print ISBN
978-981-9931-56-9
DOI
https://doi.org/10.1007/978-981-99-3157-6

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