Design and Forecasting Models for Disease Management E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The book provides an essential overview of AI techniques in disease management and how these computational methods can lead to further innovations in healthcare.
Design and Forecasting Models for Disease Management is a resourceful volume of 13 chapters that elaborates on computational methods and how AI techniques can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data on many different diseases. The main objective of this book is to demonstrate how AI techniques work for early disease detection and forecasting useful information for medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent mechanisms for helping detect early signs of diseases. Additionally, the book examines numerous machine learning and data analysis techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses various applications of image segmentation, data analysis techniques, and hybrid machine learning techniques for illnesses, and encompasses modeling, prediction, and diagnosis of disease data.
Audience
Researchers, engineers and graduate students in the fields of computational biology, information technology, bioinformatics, and epidemiology.
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Seitenzahl: 464
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Part 1: Safety and Regulatory Aspects for Disease Pre-Screening
1 A Study of Possible AI Aversion in Healthcare Consumers
1.1 Introduction to AI in Healthcare
1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario
1.3 Economic Implications of AI Aversion
1.4 Overcoming Resistance to Medical AI
1.5 Ethical Considerations and Governance
1.6 Future Outlook and Opportunities
1.7 Conclusion
References
2 A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector
2.1 Introduction
2.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)?
2.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation
2.4 Advancing Research in AI-Integrated Moral Cognitive Therapy
2.5 Conclusion
References
3 A Strategic Model to Control Non-Communicable Diseases
3.1 Introduction
3.2 Survey of Literature
3.3 Proposed Model
3.4 Conclusion
References
4 Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment
4.1 Introduction
4.2 Related Works
4.3 Proposed Model
4.4 Implementation
4.5 Results
4.6 Conclusion
References
5 Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique
5.1 Introduction
5.2 Classification of Digital Images
5.3 Methods
5.4 Segmentation and Database Extraction with Neural Networks
5.5 Applications in Medical Image Analysis
5.6 Standardize Analytics Pipeline for the Health Sector
5.7 Feature Extraction/Selection
5.8 Image-Based Forecasting Using Internet of Things (IoT) in Smart Healthcare System
5.9 IoT Monitoring Applications Based on Image Processing
5.10 Significance of Computer-aided Big Healthcare Data (BHD) for Medical Image Processing
5.11 Applications of Big Data
5.12 Conclusion
References
6 Comparative Study on Image Enhancement Techniques for Biomedical Images
6.1 Introduction
6.2 Literature Review
6.3 Theoretical Concepts
6.4 Results and Discussion
6.5 Conclusion
References
7 Exploring Parkinson’s Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis
7.1 Introduction
7.2 Literature Review
7.3 Data Review
7.4 Parkinson’s Dynamic for Patients in Train
7.5 Conclusion
References
8 A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness
8.1 Introduction
8.2 Background
8.3 Review of Previous Works
8.4 Comparative Result
8.5 Discussion
8.6 Conclusion
References
Part 2: Clinical Decision Support System for Early Disease Detection and Management
9 Diagnostics and Classification of Alzheimer’s Diseases Using Improved Deep Learning Architectures
9.1 Introduction
9.2 Related Works
9.3 Method
9.4 Result Analysis
9.5 Conclusion
Data Availability
References
10 Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection
10.1 Introduction
10.2 Related Work
10.3 Architecture Design
10.4 Experiment
10.5 Result Analysis
10.6 Conclusion
References
11 Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm
11.1 Introduction
11.2 Related Work
11.3 Proposed Workflow
11.4 Result Analysis
11.5 Conclusion and Future Work
References
12 A Robust IoT-Based Approach to Enhance Cybersecurity and Patient Trust in the Smart Health Care System: Zero-Trust Model
12.1 Introduction
12.2 Security Threats on Smart Healthcare
12.3 Smart Healthcare Security and Four-Dimension Model
12.4 Conclusion and Future Prospects
Acknowledgment
References
13 Safeguarding Digital Health: A Novel Approach to Malicious Device Detection in Smart Healthcare
13.1 Introduction
13.2 Related Work
13.3 Our Proposed Framework
13.4 Overview of Our Proposed Framework
13.5 Evaluation Procedure
13.6 Performance Evaluation
13.7 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Top ten reasons of death and disability in India.
Table 3.2 Burden of major NCDs in India.
Table 3.3 Proportion of total disease burden in 2016.
Table 3.4 2012−2016 plan of intervention to control NCD.
Chapter 4
Table 4.1 Experimental results.
Chapter 5
Table 5.1 Classification of radiation type in the imaging system.
Table 5.2 Classification of imaging systems.
Table 5.3 Various applications of digital image processing systems.
Chapter 6
Table 6.1 Results for image 1 using different image enhancement techniques.
Table 6.2 Results for image 2 using different image enhancement techniques.
Table 6.3 Results for image 3 using different image enhancement techniques.
Table 6.4 Results for image 4 using different image enhancement techniques.
Table 6.5 Results for image 5 using different image enhancement techniques.
Table 6.6 Different MSE values of the above images.
Table 6.7 Different PSNR values of the above images.
Chapter 8
Table 8.1 Algorithms used by author their papers.
Chapter 9
Table 9.1 Literature survey of Alzheimer’s diseases.
Table 9.2 Data set description before SMOTE.
Table 9.3 Dataset description after SMOTE.
Table 9.4 Experimental setup.
Table 9.5 Comparative study of the proposed algorithm.
Table 9.6 Comparative study based on Efficient Net B3 SMOTE and without SMOTE ...
Table 9.7 Comparative analysis using state of art and suggested techniques.
Chapter 10
Table 10.1 Review on AI-based stress level detection.
Table 10.2 Range of sensor value.
Table 10.3 Performance of classifier model in three class tasks.
Chapter 11
Table 11.1 Pima Indian diabetes dataset attributes.
Table 11.2 Experimental results of PIDD model.
Table 11.3 Typical example of the confusion matrix.
Table 11.4 Confusion matrix for PCA-SVM.
Table 11.5 Performance comparison of the proposed model for the testing phase.
List of Illustrations
Chapter 3
Figure 3.1 State-wise comparative status of communicable and NCDs in India. So...
Figure 3.2 The proposed model of NCD control. Source: Author’s derived model.
Figure 3.3 Proposed model of health promotion classification strategy.
Chapter 4
Figure 4.1 Colour filter array (CFA).
Figure 4.2 The proposed image editor.
Chapter 5
Figure 5.1 Block diagram of medical imaging system.
Figure 5.2 The EM spectrum of photon energy.
Figure 5.3 A digital image processing system.
Figure 5.4 Block diagram of CAD.
Figure 5.5 Diagram for extraction of planar nuclear-medicine image.
Figure 5.6 Schematic diagram of clinical ultrasound imaging.
Figure 5.7 Basic components of MRI or magnetic resonance imaging.
Figure 5.8 A three-layer artificial neural network.
Figure 5.9 Diagram of a BBN for breast cancer diagnosis.
Figure 5.10 Health informatics processing pipeline.
Figure 5.11 Relationship between AI, ML, and Deep Learning.
Figure 5.12 IoT monitoring system in healthcare.
Figure 5.13 Health information system framework.
Figure 5.14 Architectural design of machine learning based healthcare framewor...
Chapter 6
Figure 6.1 Output intensity level after contrast stretching.
Chapter 7
Figure 7.1 Motor and non-motor impairment.
Figure 7.2 Clinical data shape (2615, 8).
Figure 7.3 Supplemental clinical data shape (2223, 8).
Figure 7.4 Clinical data aggregated statistics (train and supp combined).
Figure 7.5 Clinical data aggregated statistics (train).
Figure 7.6 Value and share missing (train and support combined).
Figure 7.7 Number of unique values in clinical data (train and support).
Figure 7.8 Number of visits per patient (train and support combined).
Figure 7.9 Number of visits per patient (train).
Figure 7.10 Number of visits per patient per month (train and support combined...
Figure 7.11 Distribution of UPDRS scores.
Figure 7.12 updrs_1, updrs_2, updrs_3, and updrs_4 distribution (train).
Figure 7.13 UPDRS Score distribution by Visit_Month (train and support).
Figure 7.14 Peptide abundance.
Figure 7.15 Peptide data aggregated statistics.
Figure 7.16 Proteins data shape (232741, 5).
Figure 7.17 Protein data aggregated statistics.
Figure 7.18 UPDRS scores for 3 patients in the train data.
Chapter 8
Figure 8.1 Percentage of papers using a classifier for SQA.
Figure 8.2 Percentage of papers using a classifier for SMCA.
Figure 8.3 Predicting anxiety, depression & stress using ML.
Figure 8.4 Depression detection in twitter.
Figure 8.5 Behavioral modeling for mental health using ML algorithms.
Chapter 9
Figure 9.1 Flow diagram of the proposed method.
Figure 9.2 Description of a dataset of AD.
Figure 9.3 Confusion matrix for Efficient Net B2.
Figure 9.4 Efficient Net B2 architecture.
Figure 9.5 Confusion matrix for Efficient Net B3.
Figure 9.6 Efficient Net B3 block.
Figure 9.7 Efficient Net B3 architecture.
Figure 9.8 Training and validation loss and accuracy for Efficient NetB3.
Figure 9.9 Confusion matrix for Efficient Net B4.
Figure 9.10 Efficient Net B4 architecture.
Figure 9.11 Comparative study on metrics.
Figure 9.12 Comparative study between state-of-the-art and suggested methods.
Chapter 10
Figure 10.1 Simplified diagram of stress level system.
Figure 10.2 System overview.
Figure 10.3 Confusion matrix.
Figure 10.4 Confusion matrix for decision tree.
Figure 10.5 Comparative study of seven classifier models.
Figure 10.6 Learning curve concerning training data size.
Chapter 11
Figure 11.1 Flow diagram for the proposed research.
Figure 11.2 Boxplot of different features of PIMA dataset.
Figure 11.3 Graphical representation for frequency versus attributes.
Figure 11.4 Graphical representation for different metrics versus outcome.
Figure 11.5 Graphical representation of error rate versus value of K in KNN.
Figure 11.6 ROC curve for PCA-SVM.
Figure 11.7 A comparison study with the proposed algorithm and state-of-the-ar...
Figure 11.8 Comparison chart of different metrics of algorithm.
Chapter 12
Figure 12.1 Different types of security threats on smart healthcare.
Figure 12.2 A typical IoT-based model healthcare system.
Figure 12.3 Four-dimension smart healthcare system and its different parameter...
Chapter 13
Figure 13.1 Proposed model.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Design and Forecasting Models for Disease Management
Edited by
Pijush Dutta
Dept. of Electronics & Communication Engineering, Greater Kolkata College of Engineering and Management, Kolkata, India
Sudip Mandal
Dept. of Electronics & Communication Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India
Korhan Cengiz
Dept. of Computer Engineering, Istinye University, Istanbul, Turkey
Arindam Sadhu
Dept. of Electronics and Communication Engineering, Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex, Kolkata, India
and
Gour Gopal Jana
Dept. of Electronics & Communication Engineering, Greater Kolkata College of Engineering and Management, Kolkata, India
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-23404-2
Cover image courtesy of Pixabay.comCover design by Russell Richardson
Dedication
We dedicate the book to our family members and the contributing authors. Without their patience, understanding, and support, the completion of this book would not have been possible.
Dr. Dutta is extremely grateful to his father Late Kajal Dutta and his mother, Smt. Tulu Rani Dutta for their affection and constant support. He is thankful to his friends for their love, understanding, prayers, and continuing support to complete this book project.
Dr. Mandal is grateful to parents and family members for their continuing support.
Dr. Cengiz would like to acknowledge and thank the most important people in his life, his parents and his partner, for their support.
Dr. Sadhu wishes to thank his parents, wife, and family members for their continuous support.
Prof. Jana is grateful to his parents, wife, and family members for their continuous support. This book has been a long-cherished dream, which would not have been turned into reality without the support and love of these amazing people.
Pijush Dutta
Sudip Mandal
Korhan Cengiz
Arindam Sadhu
Gour Gopal Jana
Editors
Preface
Overview
The novel application of artificial intelligence (AI) techniques, including machine learning, and deep learning analytics for design and forecasting models for disease management, is an emerging area in computer science, computation biology, information technology, bioinformatics, bioinformatics, and epidemiology. During the last 20 years, various AI approaches have been successfully applied in diverse fields. Medicine is one of the most active domains for AI techniques, since they facilitate model diagnostics information based on statistical data and reveal hidden dependencies between symptoms of major diseases.
The present capacity to develop, evaluate, manufacture, distribute, and administer effective medical countermeasures is inadequate to meet the burden of both recurrent and emerging outbreaks of infectious diseases. When such interventions are unavailable, public health measures and supportive clinical care remain the only feasible tools to slow an emerging outbreak. Decision-making under such circumstances can be greatly improved by the use of appropriate data and advanced analytics, such as infectious disease modeling. Furthermore, these analyses can guide decision-making when medical countermeasures become available, allowing them to be used more effectively.
Forecasting is an emerging analytical capability that has demonstrated value in recent outbreaks by informing policy and enabling real-time epidemic management decisions. Since healthcare is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional healthcare practices to a smart healthcare system. New healthcare technologies provide numerous opportunities to maximize disease recognition, analysis, and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart healthcare.
This book is an essential publication that widens the spectrum of computational methods that can aid in smart disease management. It contains several statistical and AI techniques that can be used to acquire data of many different diseases.
Objective
AI techniques for early disease detection and forecasting can reveal useful information to medical experts. As such, this volume intends to serve as a resource to elicit and elaborate on possible intelligent mechanisms for helping in the modeling, prediction, and diagnosis of the infected individuals, as well as providing means for detecting early signs of the disease. As such, this book examines numerous ML and DL techniques in the biomedical field that are used for detecting and forecasting disease management at the cellular level. It discusses applications for image segmentation, classification, neural networks, and a variety of computational intelligence approaches. It also discusses DL techniques for noise elimination and filtering of brain disease and hybrid ML techniques for diabetes prediction, mental illness, and stress level monitoring approaches.
We hope this book encourages an even wider adoption of these concepts and methods to assist physicians in problem solving and stimulates research that will lead to additional innovations in this area.
Organization
The volume comprises thirteen comprehensive chapters that encompass the modelling, prediction, and diagnosis of disease data. The arrangement of the chapters guides the reader from effective modelling of different disease data to detection and diagnosis. Chapter 1 presents an overview of how AI is transforming healthcare, as well as examples of consumer resistance to it, the economic implications of AI aversion, and ethical consideration and governance. Hand in hand with the emerging technology, healthcare systems are becoming progressively complex while constantly adapting to socioeconomic, epidemiological, and demographic changes, contributing to intricate global healthcare objectives.
Chapter 2 explores the use of integrated, and systematic moral cognitive therapy as a means of administering AI in the healthcare sector. The integration of AI in healthcare has the potential to improve prognosis with the help of an enormous database, but concerns have arisen regarding the vulnerability of a large section of end-users and the ethical implications of its use.
Social inequities, poverty lack of education, and risk of lifestyle diseases are mutually complementary to each other. An unhealthy lifestyle claims maximum responsibility among the factors of becoming a victim of lifestyle diseases. Zero preventive options other than lifestyle modification have enhanced the complication of management of such mass health issues.
Chapter 3 investigates a conceptual model that can bridge the gaps between healthcare, policy, and disease prevention. Such a model can assist with the recording and reporting of data to analyze the victim’s pattern of lifestyle adoption in place of the current healthcare alternative. Color Filter Array (CFA) data can be efficiently compressed using existing image compression algorithms such as JPEG. In addition, it tested the overall performance of CFA compression methods and demosaicing algorithm selection. These CFA compression methods exploit how CFA images do not contain repetitive data introduced by demosaicing.
Chapter 4 establishes a relationship between the discovery of different CFA compression techniques and describes how the choice of demosaicing algorithm affects visual quality. Using a simple bilinear demosaicing method, CFA image compression produces better images compared to the traditional compression scheme.
Through image processing techniques, digital images can be compressed, monitored, and transmitted effectively in remote settings. Digital images have a significant effect on modern society, science, technology, and art. Some techniques utilized in image enhancement include filtering, segmentation, object recognition, and image fusion. Image processing techniques have played a crucial role in advancing healthcare, particularly in the field of smart healthcare. Chapter 5 reviews image processing for medical applications using secure smart healthcare techniques. Furthermore, the various challenges that scientists must resolve because of disease are also discussed. Image enhancement techniques engage an essential and fundamental task and interpretability of biomedical images, enabling accurate diagnosis and analysis. This chapter provides a comprehensive overview of image enhancement techniques that are specifically tailored for biomedical images. Chapter 6 presents various image enhancement methods designed specifically for biomedical imaging. The chapter evaluates and contrasts the usefulness of various approaches and the extent to which they are beneficial to the biomedical field.
Disease progression prediction is essential for patient care and therapeutic development. Clinical, genetic, and molecular data related to Parkinson’s disease (PD) are collectively gathered by the Accelerating Medicines Partnership (AMP). Chapter 7 presents parameters linked to Parkinson’s disease progression and evaluates the appropriateness of the AMP PD dataset for predictive modeling through an Exploratory Data Analysis (EDA). The onset of the Covid-19 pandemic and the practice of social isolation have led to a rise in instances of mental health issues. Advances in machine learning in the present decade have created additional opportunities for detection and the identification of mental health issues. Chapter 8 investigates the automatic prediction and diagnosis of mental illness using machine learning algorithms.
One of the most common neurodegenerative illnesses that affect older adults and mostly impair memory in the brain is Alzheimer’s disease. Chapter 9 presents three models, efficient net B2, efficient net B3, and efficient net B4 architecture-based deep learning solutions for detecting and categorizing Alzheimer’s disease.
One of the biggest issues in today’s world is mental stress. Now that age is not taken into account when calculating stress levels, it makes no difference. Depression, heart attacks, and even suicide can result from extreme stress. Stress may have an impact on many facets of our existence, such as our behavior, emotions, and capacity for thought. Chapter 10 presents a machine-learning approach for measuring a person’s stress level using three crucial parameters: body temperature, step count, and humidity.
Diabetes Mellitus is commonly known as a metabolic issue in which the body cannot utilize insulin, store glucose for energy, or produce insulin. Diabetes patients can suffer various sicknesses that incorporate kidney disappointment, stroke, visual impairment, cardiovascular failures, and lower appendage removal. Chapter 11 presents a hybrid-based machine learning approach for identifying the potential chances of contracting diabetes diseases.
The increasing adoption of smart healthcare care and Internet of Things (IoT) devices has revolutionized the healthcare industries, offering enhanced patient monitoring, diagnosis, and treatment. However, the integration of these devices into the healthcare ecosystem raises serious concerns regarding the security and privacy of sensitive medical records. Chapter 12 contributes to strengthening the overall cyber security framework within the healthcare industry, ensuring patients’ trust in smart healthcare IoT devices and fostering the continued growth of remote patient monitoring.
The proliferation of smart healthcare systems has introduced a new era of patient care, leveraging interconnected medical devices to improve monitoring and treatment. However, this enhanced connectivity also exposes healthcare environments to potential cyber security threats, including malicious devices. Chapter 13 shows how to employ advanced machine learning algorithms, anomaly detection techniques, and behavioural profiling to continuously monitor and analyze the behavior of interconnected devices. By establishing a baseline of normal device behavior, our system can identify deviations indicative of potential malicious intent.
We are deeply grateful to everyone who helped with this book and greatly appreciate the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during its publication.
Pijush Dutta
Sudip Mandal
Korhan Cengiz
Arindam Sadhu
Gour Gopal Jana
Editors
Part 1SAFETY AND REGULATORY ASPECTS FOR DISEASE PRE-SCREENING
1A Study of Possible AI Aversion in Healthcare Consumers
Tanupriya Mukherjee1 and Anusriya Mukherjee2*
1Department of MBA, Budge Budge Institute of Technology, Kolkata, India
2Department of Basic Science and Humanities, Budge Budge Institute of Technology, Kolkata, India
Abstract
Computer systems, that are capable of executing activities that require human intelligence, often referred to as Artificial Intelligence (AI), are making their mark in all industrial territory lately. The fact that enormous data sets can be used to teach these AI systems to recognize patterns and make predictions accordingly, is highly advantageous adding the agility of the present humanized systems at work. In recent times, it has been noted that artificial intelligence and machine learning technology have reshaped the healthcare sector as well. AI has the potential to transform the healthcare industry by increasing efficiency, lowering costs, and improving the prognosis for patients. Integrating AI in healthcare presents a few hurdles, of which the two most important are meeting compliance requirements and resolving issues of confidence with machine learning outcomes. Despite these obstacles, introducing machine learning, artificial intelligence and other technologies to the healthcare business has resulted in various benefits for both healthcare organizations and the patients they serve. Both machine learning and AI have shown an array of advantages in the healthcare industry by optimizing processes and assisting with routine chores, as well as assisting users in promptly finding solutions to critical concerns, enabling improved services for patients as well as consumers. Most healthcare providers are offering user-driven experiences and increasing operational efficiency in making the best possible use of collected data, assets, and resources by evaluating data trends, enhancing coherence and improving the accomplishments of clinical and operational procedures. Yet, even after exiting a major part of two decades in the medical industry, insufficient information exists about consumer perceptions towards AI in medical treatments and procedures. Our study is aimed at learning consumers’ apprehensiveness to embrace AI-assisted healthcare in both tangible and hypothetical choices, be it independent or collaborative evaluations.
Keywords: Artificial intelligence, machine learning, automated healthcare application, healthcare consumer, consumer behavior
1.1 Introduction to AI in Healthcare
The advent of artificial intelligence (AI) has grown exponentially to have practical influence in myriads of segments for over the last few decades and continues to permeate into various sectors of the global economy in recent years. The promise of automation, improved efficiencies, and reduced human error has created a rush of innovations integrating AI systems into their core offerings. Seeping deep into various sectors like healthcare, education, banking, and so on, the enormous spectrum of AI’s offerings is ruling the roost by extending benefits ranging from mundane tasks, such as speech recognition and decision-making, to financial forecasting and advanced medical diagnosis. The world is gradually acknowledging the potential transformative power of AI-based diagnostic assistance, predictive tools, patient monitoring, and surgical robotics. The use of AI in healthcare is projected to register significant growth in the future. AI’s essence in the health sector is underpinned by the ability of these systems to consume vast populations of information, emulate human cognition, and improve themselves (Obermeyer et al., 2016) [1]. Consequently, these abilities have positioned AI as a powerful tool redefining access, affordability, accuracy, and even our understanding of healthcare. Deep learning algorithms are being trained to recognize patterns in radiology images and predict diseases even before any physical symptoms manifest. Google’s DeepMind achieved over 94% accuracy in diagnosing eye diseases, a similar degree of precision to top human experts (De Fauw et al., 2018) [2]. This improvement in early detection would significantly enhance treatment outcomes. Additionally, AI has shown compelling capabilities in surgery. Robotic-assisted surgeries have become more precise, stable, and minimally invasive as compared to conventional surgical techniques (Barr et al., 2018) [3]. The Da Vinci Surgical System heralds a new era in precision medicine by utilizing AI functionalities to aid surgeons in intricate surgical procedures, thereby improving patient safety. AI has also proven instrumental in innovative drug discovery, reducing the length and cost of R&D cycles. An AI system developed by Insilico Medicine designed a novel drug in just 46 days, a process that would usually take a few years of human research (Zhavoronkov et al., 2019) [4] to design medications and vaccines. However, like any other technology, AI is a double-edged sword-eliciting both apprehension and enthusiasm simultaneously and there is still potential AI aversion among consumers that may stifle this evolution. In particular, it has been observed in this study that the older generation exhibits aversion towards AI more prominently than the young demographic. Most of these systems rely on vast amounts of data for accuracy which raise critical concerns over patient privacy and data security. Breaching these might cause immense harm (Song et al., 2019) [5]. While policies and procedures are being developed to mitigate these risks, they remain a persistent concern. Additionally, the question of oversight and accountability also arises. If an AI system makes a wrong diagnosis or surgical mistake, who is to be held accountable? These ethical and legal dilemmas further complicate AI’s integration into healthcare (Sperry et al., 2018) [6]. To demystify this conundrum, this book chapter aims to understand and examine the underlying factors behind possible AI aversion in healthcare consumers.
1.1.1 The Role of AI in Transforming Healthcare
Artificial Intelligence has become an integral transformative element of healthcare delivery by revolutionizing medical care, from improving diagnosis and prescribing treatments to streamlining hospital operations and enhancing patient experiences. A recent report by Accenture (2021) states that AI applications, when utilized fully in healthcare, could save up to $150 billion annually for the US alone by 2026 (Basole et al., 2021) [7].
AI’s role in advancing medical research - AI has demonstrated remarkable capabilities in advancing medical research. By analyzing vast quantities of data, AI can predict disease outbreaks, accelerate drug discovery, and provide new insights into disease mechanisms. For instance, AI technology assisted researchers in identifying potential treatments for COVID-19 significantly quicker than traditional research methods would have allowed (Bullock et al., 2020) [8].
AI’s assistance in diagnostics and treatment - AI’s contribution extends to improved patient diagnostics and treatment plans. Machine learning models can analyze complex medical images with higher accuracy and in less time than human clinicians, leading to quicker diagnosis and treatment initiation. For instance, researchers at Stanford University developed an AI model that achieved a performance comparable to human radiologists in detecting pneumonia in chest X-rays (Rajpurkar et al., 2018) [9].
Tackling healthcare inefficiency - Furthermore, AI is effective in tackling the inefficiency in healthcare. It reduces paperwork, expedites administrative duties, and optimizes the schedule of health professionals, resulting in time and cost savings. As (Satava et al., 1995) [10] highlight, about 36% of healthcare tasks—mostly managerial and administrative — can be automated, thereby freeing up medical staff for patient-facing duties.
The Echo Dot case - Patient experiences are significantly enhanced through AI as well. A notable example is the use of Amazon’s Echo Dot by NHS England to supply information to patients at the comfort of their homes. This initiative not only eased the burden on healthcare professionals but also boosted patients’ morale and wellbeing (Hasanuzzaman et al., 2019) [11].
The above examples cited demonstrate how AI integrated tools can work alongside traditional healthcare delivery methods to deliver optimum patient outcomes. To fully actualize AI’s potential in healthcare, it requires continuous exploration and investment in AI capabilities, revised regulatory guidelines and proactive stakeholder engagement.
1.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare
Healthcare in the modern age is increasingly becoming intertwined with burgeoning technology, the potential of which holds tremendous promise for improving patient outcomes on one hand and challenges that hinder its full realization on the other, whilst posing significant challenges.
Benefits of AI implementation in healthcare: The integration of AI in healthcare offers a plethora of benefits. AI can streamline diagnostic procedures, make predictions about health trends, and contribute to personalized medicine. Regular health monitoring can be performed more efficiently with AI algorithms, reducing the workload of healthcare professionals. Deep learning techniques, a subset of AI, can help uncover complex patterns in voluminous datasets, improving diagnosis accuracy. For instance, AI algorithms have been used to accurately diagnose skin cancer, matching or even surpassing dermatologists’ success rates (Esteva et al., 2017) [12]. AI has shown promise to enhance early detection and prevention of diseases, thereby improving patient outcomes. AI can also be pivotal in the analysis of healthcare trends. Various researchers have successfully used AI to predict disease outbreaks. For example, BlueDot, a Canadian AI firm, correctly predicted the COVID-19 outbreak a week earlier than the Centers for Disease Control and Prevention (CDC) (Noah et al., 2000) [13]. Moreover, AI’s ability to process and learn from a patient’s medical history opens doors to personalized medicine, tailored to each patient’s unique genetic make-up. This can substantially increase treatment effectiveness and reduce potential side effects (Krittanawong et al., 2018) [14]. Challenges of AI implementation in healthcare: Despite the immense benefits, AI implementation in healthcare also comes with substantial challenges. The complex nature of medical data with numerous variables and the need for meticulous data labeling makes it a difficult task. Incorrect data labeling in AI-based systems can lead to inaccurate clinical decisions (Atkins et al., 2021) [15]. Moreover, patient privacy concerns are also pertinent given that AI operates using vast amounts of patient data. Strict regulatory checks are imperative to maintain patient anonymity and avoid data misuse. Regulatory bodies must establish safeguards to maintain data security and ensure that AI applications adhere to privacy laws [16]. Furthermore, while AI systems’ decision-making is based on algorithmic patterns, these algorithms are not infallible. They may lack the clinician’s expertise in interpreting unusual clinical scenarios, emphasizing the importance of clinician oversight on AI-based decisions. Also, the initial financial cost of integrating AI technology into healthcare systems can be high, which may limit its adoption, particularly in resource-constrained settings.
1.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis
Artificial intelligence (AI) in medicine is a revolutionary concept that has the propensity for wide-reaching implications. However, acceptance and adoption of this innovation remain variable, thus necessitating evaluation of consumer receptivity towards AI in medicine by compare consumer attitudes and receptivity, drawing insights from different demographic segments across the globe. Many consumers display a remarkable openness to AI in medicine, particularly in routine tasks and facilitative roles (Haynes et al., 2020) [17]. Foremost, AI provides unmatched efficiency and accuracy when it comes to medical image analysis, medical records examination, diagnostics, and drug discoveries (Topol et al., 2019) [16]. In particular, consumers view AI as a complementary tool for healthcare professionals, improving precision and allowing more time for direct patient care (Shorvon et al., 2018) [18]. However, the receptivity becomes significantly lowered when AI is introduced into more involved, perceived high-stake roles such as surgical procedures and disease prognosis. Medical decisions significantly impact personal health and wellbeing, thus the reluctance to entrust such responsibilities to AI. This contrast suggests that the scope of acceptance for AI in medicine appears to hinge on its application magnitude. Medical practitioners’ endorsement or resistance towards AI in medicine critically influences consumer receptivity. For example, a global survey revealed that those within healthcare were positive regarding AI’s dynamic capabilities but remained reserved about its ethical implications and data security (Arnold et al., 2017) [19]. Their main concern revolves around patient’s confidentiality and the potential misuse of personal data, which require stringent regulations (Braunack-Mayer et al., 2010) [20]. Significantly, demographic factors influence consumer receptivity towards AI in medicine. Younger consumers appear more accepting of AI’s role in healthcare provision, while older individuals are skeptical, perhaps due to unfamiliarity or mistrust in technology. Geographic location is also crucial; countries with robust technological infrastructure and awareness show a higher acceptance rate of AI in healthcare (Williams et al., 2020) [21]. Different cultural attitudes towards AI in the medical field also need consideration. Countries where technology is deeply enmeshed in societal daily functions, such as Japan and South Korea, show greater acceptance of AI in medicine than those where technology is seen as a potential threat to traditional ways of life. Thus consumer receptivity towards AI in medicine is a multifaceted issue that depends on AI’s role, professionals’ endorsement, demographic factors, and cultural perspectives. Although AI provides numerous benefits, its adoption in the medical field will remain variable unless it satisfies these different cohorts’ particular concerns. Balancing the benefits and potential risks is key to fully unlocking AI’s potential in healthcare. The robustness of regulations governing AI’s use and data protection measures will be crucial moving forward. As we continue to explore AI’s capabilities, it is paramount that digital innovation functions ethically, securely, and to the advantage and safeguarding of patients.
1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario
In the world increasingly inured to digital intervention in practically every facet of existence, it would seem natural for AI to permeate healthcare services which holds substantial promise, the challenges are equally compelling. As we stand on the brink of what could be a revolutionary change in healthcare delivery, it is paramount to confront these challenges proactively and constructively, to ensure that AI is a tool that augments human ability, rather than complicating it. Recent developments reveal significant consumer reluctance in harnessing AI’s potential advantages in healthcare. Despite the apparent advantages, the healthcare industry’s AI potential is met with considerable skepticism and resistance from the very beneficiaries it seeks to serve. Exploring the reasons behind this reluctance provides insights into various factors such as privacy concerns, trust issues, and potential job loss (Asmundson et al., 2020) [22].
From an economic perspective, the high costs associated with acquiring, implementing, and maintaining AI systems can be a deterrent for many healthcare organizations. While AI promises significant long-term gains, the short-term financial strain may be too high for some, particularly in resource-limited settings. Consequently, measures such as public-private partnerships, shared-risk models, and cost-effectiveness studies could be beneficial (Cassar et al., 2020) [23].
Healthcare AI is also not immune to algorithmic bias, which may result in unethical and harmful treatment recommendations. Panch et al. warn of biases arising due to the misrepresented of certain demographic groups in training data, leading to potentially discriminatory practices (Panch et al., 2019) [24]. Steps to mitigate this bias might encompass using diverse datasets for training AI models, regular model audits, and comprehensive bias training for AI users in healthcare. Nonetheless, studies indicate an upturn in consumer acceptance of AI in healthcare is foreseeable, given specific conditions. Greater transparency, stronger regulations protecting privacy, and emphasizing the importance of these technologies to augment rather than replace human effort would lead to a paradigm shift in consumer behavior (Gerotziafas et al., 2020) [25].
Another prominent impediment towards consumer acceptance of AI in healthcare is privacy concerns. The most formidable challenges are the issue of data security in operation of AI technologies due to processing of large amounts of personal data, causing significant privacy apprehensions for consumers. With AI, the use of EHRs (electronic health records), predictive modeling, and algorithms necessitates the collection and analysis of vast amounts of patient data (Majeed et al., 2019) [26]. This can then prompt concerns about the breach of patient confidentiality, data mismanagement, and misuse, posing serious ethical dilemmas. Moreover, varying legal constructs around data privacy globally further intensify these challenges. As suggested by Blease et al., efforts towards strengthening security protocols, and adhering to a standardized international regulatory framework could pave the way forward [27]. The Cambridge Analytica scandal and similar instances of data breaches further exacerbate these concerns Greenwald et al., 2013) [28].
Closely related to privacy are the issues of trust and ethics. Trust in AI technologies tends to be low due to potential implicit biases in algorithmic training and decision-making. Ethical dilemmas surrounding the deployment of AI technologies further underline the need for transparent AI healthcare systems (Nemitz et al., 2018) [29].
In addition to these, the possibility of job losses has cast a shadow over AI healthcare’s prospect. With machines potentially usurping medical professionals’ roles, workers fear a decline in employment opportunities (Chui et al., 2016) [30]. Another difficulty in AI implementation stems from its integration into existing healthcare systems. Healthcare organizations often confront resistance to change from staff who are unaccustomed to or fearful of new technology. The issue is complex and multifaceted, involving factors such as lack of digital literacy and the fear of job redundancy (Akter et al., 2017) [31]. Addressing this may require an investment in training and educating healthcare providers to acclimate them to the new environment.
Additionally, there are concerns about the transparency, as high-stakes decisions in healthcare require understanding the underlying reasoning, the ‘black-box’ nature of some AI models can be a considerable barrier (Scott et al., 2019) [32]. Research on explainable AI models and regulatory mandates for transparency may help alleviate these concerns.
Automation in healthcare thus remains a terrain marked by both potential and apprehension. It is vital that the apprehensions be addressed earnestly, to establish a future where AI and humans coexist symbiotically, furthering our common goal of improving the quality of healthcare services.
1.2.1 Top Factors Influencing Consumer Resistance to Medical AI
Understanding, addressing and mitigating the factors of consumer resistance to medical will pave the way for the mainstreaming of AI in healthcare. With proper regulations, transparency, and communication, integrating the vast potentials of AI in healthcare will be a foreseeable future.
Lack of Understanding and Trust: The most frequently cited reason for resistance to medical AI is the lack of understanding and subsequent distrust due to the complex nature which often generates fear and suspicion amongst the lay population, creating resistance to its integration with healthcare (Jordan et al., 2019) [33].
Privacy Concerns: According to N. Bostrom (2014), Concerns about personal privacy rank high in the list of consumer resistances to medical AI as most clients express apprehension about the potential misuse of personal medical data by unscrupulous entities (Bostrom et al., 2014) [34].
Fear of Job Displacement: Both medical and non-medical staff fear that AI might supplant human roles in the healthcare sector creating a significant source of resistance against medical AI (Jungmann et al., 2020) [35].
Transference Errors: Floridi et al. conducted a study (2018) found that possible data transference errors feed into the resistance against medical AI (Floridi et al., 2018) [36].
Over-reliance on Technology: As clients, people value the human touch in healthcare, and the perceived loss of this aspect through over-reliance on technology might degrade the human aspect of medical can foster considerable resistance (Arya et al., 2019) [37].
Costs: The potential costs associated with integrating AI into healthcare could also evoke resistance in patients with the concern that these costs might reflect in their medical bills, creating opposition to AI (Goodman et al., 2014) [38].
Potential Cyberattacks: The susceptibility of AI platforms to threat of cyberattacks and hacking raises security concerns, fostering resistance amongst consumers leading to AI aversion (Topol et al., 2019) [39].
Liability Issues: Ensuring accountability for medical errors made by AI systems is a significant issue in healthcare, and the nebulous nature of AI accountability can drive away potential users (Haugsten et al., 2023) [40].
Potential Misdiagnosis: The gravity of a misdiagnosis, especially in crucial health cases, can foster significant opposition (Kong et al., 2020) [41].
Limited Access: Lastly, the limited access to AI technology in some regions fosters resistance against medical AI. The perceived elitism of AI healthcare can deter the broader population from accepting AI (Brynjolfsson, 2014; Challen, 2019; Price, 2021) [42–44].
1.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare
Fostering a culture that embraces AI and automation, rather than fearing their potential impacts, is critical. Navigating the psychological barriers and concerns associated with AI adoption in healthcare isn’t an overnight achievement but a journey towards a more efficient, effective, and empathetic healthcare provision. (Hafner et al., 1995) [45], only 39% of healthcare providers understood AI’s capacities and potentials, signaling psychological barriers that impede the adoption [45]. The inexplicable capabilities of AI (Rao et al., 2013) [46] and the lack of comprehensive knowledge about its inner workings, breeds anxiety among healthcare professionals (Castelvecchi et al., 2016) [47]. This ‘black box’ nature of complex AI systems is perceptually intimidating, which can impede its acceptance in a field where life-altering decisions are the norm.
Another psychological concern is the fear of replacement and this perceived potential of job loss due to automation tends to foster resistance towards AI, (Mahdawi, et al., 2017) [48]. However, research suggests that AI will augment human endeavors in healthcare by freeing up time for better patient interaction and care, fostering a patient-physician relationship that no machine can replace.
Trust and reliability form another significant barrier as healthcare providers themselves struggles to trust an algorithm over their experience and intuition. Building certainty and reliance concerning AI’s accuracy, security and precision is crucial for its seamless integration into healthcare systems and resist aversion (Ross et al., 2009) [49].
Also, ethical considerations loom around AI’s utilization in healthcare. Professionals worry about AI systems making errors, data privacy breaches, or target patients with machine-driven biases. Effective AI planning and accountability measures in healthcare should address such apprehensions to foster confidence in its adoption.
Lastly, the concern of increased workload is palpable. The potential demand for additional skills to operate these sophisticated AI systems might spawn an initial additional workload for the healthcare workforce. The upskilling demand can be offset via creating training programs, fostering digital literacy, and establishing ongoing education pertaining to AI technologies.
Impressively, views about AI in healthcare are not uniformly gloomy, since automated healthcare can actually expedite diagnosis, treatment planning and enhance patient care. There exists an understanding for AI’s potential in mitigating medical error and enhancing patient safety. Ironing out the psychological wrinkles requires strategies that alleviate fear, enhance trust, tackle ethical issues, and balance workload expectations. Emphasis should be placed on transparency, understanding, and clear communication about AI’s role, scope, limitations, and benefits. Interestingly, a change-centric, proactive approach can successfully navigate these psychological barriers. Healthcare providers must be involved in AI policy-making, system design, and evaluation. More in-depth, accessible knowledge about AI can alleviate fears and misconceptions. Interactive AI training, comprehensive user support, and practical demonstrations can reinforce trust and confidence in its capabilities, thus accelerating healthcare’s digital transformation.
1.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services
AI-based healthcare services hold immense promise. However, without catering to consumers’ concerns and fears, we risk alienating them, fuelling a continued aversion to such technology. Hence it is imperative to delve into the complexities surrounding this evolving discourse, basing on several case studies grappling with this issue.
The aversion towards AI-based healthcare services in certain sectors of the population continue to resist the increased onslaught of healthcare AI, leaning towards the familiar and traditional healthcare models (Rodriguez et al., 2020) [50] which involves using complex algorithms and software to mimic the human cognitive function in analyzing complex medical data (Adadi et al., 2018) [51]. A study by Ipsos MORI (2018) (Ipsos et al., 2017) [52] found a lack of trust among consumers regarding the use and protection of their health data by AI systems. They worry about data breaches, misuse or theft of personal information, which may lead to identity theft (Bello-Orgaz, et al., 2016) [53]. Consumers are understandably concerned about how their data is stored, used and secured, often leading to skepticism and resistance (Kostkova et al., 2016) [54]. One noteworthy case reflecting this issue is the collaboration between Google and the National Health Service (NHS). Google’s DeepMind Health was accused of improper access to patient records causing privacy activists and patients to question the integrity of handing over their healthcare data (Powles et al., 2017) [55]. This example illustrates how a breach of trust can disrupt AI adoption in healthcare. Moreover, a level of mistrust is directed towards the AI algorithms itself, leading to misdiagnosis or incorrect treatment suggestions (Makin et al., 2019) [56]. The study cites a case where an AI chatbot wrongly advised a patient, demonstrating that these concerns are not merely hypothetical.
Adding to that is the fear of the perception of AI as a ‘faceless’ entity has struck a discordant note with consumers who value the human touch in healthcare provision. A study by Accenture (2017) (Jeffery et al., 2017) [57] found that consumers exhibited an increased level of unease with AI-based services. A case study of a Boston-based hospital shows how older patients found it difficult to adapt to an AI system, longing for personal interactions (Rodriguez et al., 2020) [50]. This resistance stemmed from a fear of losing the vital human interaction like the compassion and understanding of a human clinician that forms an integral part of the consultation and healing process, that AI is yet to encapsulate (Luxton et al., 2014) [58]. The real-world case studies of Babylon Health, an AI-based app designed to provide health consultations, reiterate the above issues. Despite the app’s capabilities, it was met with considerable consumer resistance in the UK (Petkovic et al., 2019) [59]. Users echoed concerns over the impersonal nature of consultations, indicating a preference for human doctors who can empathize and offer comfort, suggesting AI cannot replace the human healthcare provider.
Sadly, these fears have been somewhat validated by actual occurrences or nascent possibilities. It is crucial that healthcare providers, technology developers and respective legislation synchronize efforts to bridge the gap between AI advancements and consumer sentiments. Greater transparency, stronger data security measures, and a balanced model of care are pertinent in turning the tide towards a more accepting environment. The forward march of technology is inexorable but assessing and recognizing consumer sentiments and concerns will ensure a smoother and more beneficial integration of AI into the healthcare service landscape.
1.2.4 Impact on Consumer Decision-Making
Consumer decision-making forms the backbone of the modern competitive business world. While the advent of AI in healthcare presents promising prospects, its full potential would only be realized once the consumer’s innate preference for human interaction and aversion to AI is understood and addressed. The key lies in bridging the gap between healthcare consumers and AI technology, creating a synergistic amalgamation of man and machine. There exists an increasing level of consumer engagement with AI in healthcare. A growing interest has been observed towards wearable technology, virtual assistants and patient portals integrated with AI, with consumers showcasing active involvement in tracking their health (Arnold et al., 2017) [19]. Consumers, therefore, are seemingly amicable to adopt this new healthcare trajectory.
In parallel, there prevails a prominent sense of skepticism, shaping into AI aversion among a faction of healthcare consumers (Kumar et al., 2017) [60]. This cognitive dissonance among healthcare consumers cements the argument that while these impressive technological advancements can be beneficial, they have also brewed concerns around privacy, preference for human interaction, and fear of the machine-made decisions.
User-oriented studies have continually demonstrated that despite the benefits of AI, people prefer human decision-making in sensitive areas, like healthcare (Dietvorst et al., 2015) [61]. This aversion potentially impacts the domains of consumer decision-making. It is crucial to remember that healthcare decisions are innately personal and typically involve a significant emotional aspect. It can be argued that AI, despite its extraordinary capabilities, lacks the ability to understand, empathize, and respond to these emotional factors that contribute towards making healthcare decisions (Botrugno et al., 2021) [62].
However, this does not mean that the future of AI in healthcare is bleak. Consumer aversion towards AI can be addressed through measures like transparency in AI operations, robust research and development in the area of AI integration and improving AI’s ability to communicate empathetically. Leveraging technology to humanize AI, sculpting it to understand and respond empathetically to emotional factors, could potentially change the tide of AI aversion in healthcare consumers [63]. In the end, consumers hold the reins of their healthcare journey, and technology is but a tool in their hands to make this voyage smoother and more informed.
1.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis
AI aversion is a genuine concern that has significant implications on the decision-making process of healthcare consumers as it can provide customized experiences for consumers, significantly influencing their behavioral tendencies. For instance, AI can utilize customer data to generate personalized product recommendations, which potentially sways the consumer’s purchasing decisions (Ngai et al., 2009) [64]. Consequently, it may negatively impact these companies’ sales and growth, illustrating the far-reaching implications of AI aversion (Brinker et al., 2020) [65]. Companies that excessively depend on AI technology might alienate a segment of their customer base who deem AI as a replacement rather than a complement to human service (Van et al., 2019) [66]. Furthermore, AI aversion can catalyze a resistance to change by consumers. Many individuals, comfortable in their routines, might refuse to incorporate AI into their decision-making process (Ransbotham
