Fundamentals of AI for medical education, research and practice / Sameer Mohommed Khan.

Format:

Book

Author/Creator:

Khan, Sameer Mohommed, author.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Medicine--Study and teaching (Higher).

Medicine.

Physical Description:

1 online resource (578 pages)

Edition:

First edition.

Place of Publication:

London, England : Academic Press, [2025]

Summary:

Fundamentals of AI for Medical Education, Research and Practice provides a comprehensive introduction on all aspects of AI application in healthcare, ranging from medical education to diagnostic medicine.

Contents:

Front Cover

Fundamentals of AI for Medical Education, Research and Practice

Copyright

Dedication

Contents

About the author

Preface

Acknowledgments

1- INTRODUCTION TO AI IN HEALTHCARE

1 - Timeline of artificial intelligence

Introduction

Definition of AI

Stages of artificial intelligence

Three stages of artificial intelligence

Basic working of AI

Steps of AI working

Key components of AI

The five key components of AI include

Learning

Reasoning

Problem-solving

Perception

Language understanding

Types of AI

AI systems based on capabilities

Narrow AI

Artificial general intelligence (AGI)

Artificial super AI(ASI)

Based on the learning capabilities AI is also classified as

Machine learning

Deep learning

Reinforcement learning

AI systems based on the functionality

Reactive machines AI

Limited memory AI

Theory of mind

Self-aware AI

AI systems based on application

Natural language processing

Computer vision

Robotics

History of AI

The Greek Connection

Enigma broken with AI (1942)

Birth of AI (1950-56)

Alan Turing and his contribution to artificial intelligence

Turing machine: A model of general-purpose computer

Test for machine intelligence by Alan Turing (1950)

John McCarthy, the father of artificial intelligence

Golden age of AI (1956-74)

The perceptron (1957)

The emergence of deep learning (1962)

The first chatbot-Eliza (1964)

Stanford conference: Shakey

AI winter (1974-93)

Era of GPU (1990-2000)

Man versus machine-Deep Blue beats chess legend (1997)

The birth of machine learning (1997)

Kismet: The first social Robot (1998)

Artificial intelligence robot (1999)

Siri: The virtual assistant (2008).

The Q/A computer system-IBM Watson (2011)

Alexa (2014)

Tianhe-2 (2014)

The first robot citizen-Sophia (2016)

AlphaGo (2016)

Bidirectional encoder representations from transformers (BERT) 2018

Cimon

The revolutionary tool for automated conversations-ChatGPT (2018)

DALL-E (2021)

Microsoft Copilot (2023)

Summary

Bibliography

2 - Understanding artificial intelligence (AI) in healthcare and medical education

Need for AI in healthcare and medical education

Key factors that have led to the adoption of AI in healthcare and medical education include

Technological leap

The real predicament: Big data

Demand for personalized medicine

Diagnostic and therapeutic advancements

AI in geriatric healthcare: The new horizon

Employee burnout

Patient-centric care

Emerging healthcare challenges

Augmenting medical education

Better equipped healthcare professionals

Goals of AI in healthcare

Major goals of AI in healthcare include

Develop problem-solving ability

Promote synergy between humans and AI

Encourage social intelligence

Contemplating the future

In pursuit of knowledge

AI in healthcare

Major areas that AI is being utilized in healthcare are

Predictive analytics

Diagnosis and treatment

Drug discovery and development

Remote patient monitoring

Healthcare management

Robotic surgery

AI in education

Major role of AI in medical education include

Learning intensified

Medical training

Personalized learning

Automated grading and feedback

Increased flexibility and global access

Data analysis and predictive modeling

Language learning and translation

AI reduces cost

AI improves educational equity

AI metaverse

3 - Overview of AI technologies and their impact on healthcare.

Introduction

AI technologies in healthcare

Artificial intelligence (AI) technologies that are particularly significant for the healthcare include

Machine learning process workflow involves a series of steps

Data collection

Data preparation

Choosing and training the model

Model optimization

Model evaluation

Model deployment

Types of machine learning

Supervised learning

Unsupervised learning

Semisupervised learning

Applications of ML in healthcare

Smart health record

Personalized treatment options

Disease prediction

Disease identification and diagnosis

Medical imaging

Deep learning (DL)

Deep learning and the human brain

Artificial neural network (ANN)

Basic structure of ANN

Input layer

Hidden layer

Output layer

Types of deep learning

Perceptron

Feed-forward neural networks (FNN)

Clinical diagnosis

Medical image processing

Clinical decision-making

Multilayer perceptron (MLPs)

Applications of multilayer perceptron

Pattern and speech recognition

Self-care activities

Convolutional neural network (CNN)

Convolutional neural network (CNN).

Recurrent neural network (RNN)

Recurrent neural network (RNN)

Internet of things (IOT)

Sensors/devices

Connectivity

Data processing

User interface

Internet of Medical Things (IoMT)

Robotic process automation (RPA)

Applications of RPA in healthcare

Some of the important applications of RPA have been described below

Rule-based expert system

Natural language processing (NLP)

Computer vision (CV)

AI technologies used in medical education

Intelligent tutoring systems (ITS)

Virtual simulations and augmented reality

Experiential learning

Generative AI technologies (GenAI)

Students can receive improved feedback and assessment

Assists students in engaging with simulated patient scenarios

Impact of using AI technologies in healthcare

Benefits and challenges

4 - Ethical and regulatory considerations in AI adoption in healthcare

AI ethics

Need for AI ethics

Ethical considerations of medical AI

Data bias

Privacy issue

Accountability and transparency

Reliability and trust

Ethical principles for AI technology in healthcare

Autonomy

Trustworthiness

Data privacy

Accountability and liability

Optimization of data quality

Accessibility, equity, and inclusiveness

Collaboration

Validity

Fairness

AI ethics in medical education.

Ethical problems which might arise

Actions must be performed to resolve moral dilemmas

Preparing academic members and students for moral AI usage

Standardization of software for detecting plagiarism

Research ethics pertaining to AI

Ethical issues that might come up

Bias

Transparency

Accountability

Actions must be made to resolve moral dilemmas

Informed consent

Research ethics committees (REC) and institutional review boards (IRB)

Responsibilities in AI research ethics

Confidentiality

AI ethics in clinical practice

Informed consent for clinical procedures

Security and transparency

Biases and fairness in algorithms

Data that is representative and diverse

Auditing and validating algorithms

Instruction for patients and clinicians

Regulations in AI usage

Role of the World Health Organization (WHO)

Protect autonomy

Promote human well-being, safety, and public interest

Ensure transparency

Foster accountability

Ensure inclusiveness and equity

Promote AI.

UNESCO: Global AI Ethics and Governance Observatory.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9780443335853

0443335850

OCLC:

1492206545