Explainable and Responsible Artificial Intelligence in Healthcare E-Book
168,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book presents the fundamentals of explainable artificial intelligence (XAI) and responsible artificial intelligence (RAI), discussing their potential to enhance diagnosis, treatment, and patient outcomes.
This book explores the transformative potential of explainable artificial intelligence (XAI) and responsible AI (RAI) in healthcare. It provides a roadmap for navigating the complexities of healthcare-based AI while prioritizing patient safety and well-being. The content is structured to highlight topics on smart health systems, neuroscience, diagnostic imaging, and telehealth. The book emphasizes personalized treatment and improved patient outcomes in various medical fields. In addition, this book discusses osteoporosis risk, neurological treatment, and bone metastases. Each chapter provides a distinct viewpoint on how XAI and RAI approaches can help healthcare practitioners increase diagnosis accuracy, optimize treatment plans, and improve patient outcomes.
Readers will find the book:
- explains recent XAI and RAI breakthroughs in the healthcare system;
- discusses essential architecture with computational advances ranging from medical imaging to disease diagnosis;
- covers the latest developments and applications of XAI and RAI-based disease management applications;
- demonstrates how XAI and RAI can be utilized in healthcare and what problems the technology faces in the future.
Audience
The main audience for this book is targeted to scientists, healthcare professionals, biomedical industries, hospital management, engineers, and IT professionals interested in using AI to improve human health.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 598
Veröffentlichungsjahr: 2025
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
1 Uncapping Explainable Artificial Intelligence–Centered Reinforcement Learning and Natural Language Processing in Smart Healthcare System
1.1 Introduction
1.2 XAI-Based Reinforcement Learning in Smart Healthcare Systems
1.3 Natural Language Processing in Smart Healthcare Systems
1.4 Incorporation of XAI-Based RL and NLP
1.5 Synergies Between XAI, RL, and NLP in Healthcare
1.6 Patient Engagement and Care Management in Health Sector: XAI and NLP Methods
1.7 Conclusion and Future Scope—Implications for Healthcare Practice
References
2 Explainable and Responsible AI in Neuroscience: Cognitive Neurostimulation
List of Abbreviations
2.1 Introduction
2.2 Foundations of Cognitive Neurostimulation
2.3 Cognitive Neurostimulation Techniques
2.4 Explainable AI in Cognitive Neurostimulation
2.5 Responsible Artificial Intelligence in Cognitive Neurostimulation
2.6 Interdisciplinary Collaboration
2.7 Case Studies in Explainable and Responsible AI in Cognitive Neurostimulation
2.8 Future Perspective
2.9 Conclusion
Acknowledgments
References
3 Diagnostic and Surgical Uses of Explainable AI (XAI)
List of Abbreviation
3.1 Introduction
3.2 Uncertainty of CNN Model Prediction by Leveraging XAI
3.3 Algorithms of XAI Techniques
3.4 Need for Using XAI
3.5 Scope of AI Surgery [25]
3.6 Limitations and Concerns
3.7 Conclusion and Future Implications for Surgeons and Future Perspective
References
4 Osteoporosis Risk Assessment and Individualized Feature Analysis Using Interpretable XAI and RAI Techniques
4.1 Introduction
4.2 Responsible Artificial Intelligence (RAI)
4.3 Explainable Artificial Intelligence (XAI)
4.4 Key Principles of Explainable Artificial Intelligence (XAI)
4.5 Radiomics, Machine Learning, and Deep Learning
4.6 Diagnosis of Osteoporosis
4.7 General Workflow of AI-Based BMD Classification in CT
4.8 Conclusion
References
5 Spinal Metastasis—Imaging Using XAI and RAI Techniques
List of Abbreviations
5.1 Introduction
5.2 Spinal Metastasis: Need of Artificial Intelligence for Imaging
5.3 Artificial Intelligence Imaging Using XAI and RAI Technique
5.4 Challenges and Future Directions and Research Needs
5.5 Conclusion
References
6 Explainable Artificial Intelligence and Responsible Artificial Intelligence for Dentistry
6.1 Introduction
6.2 The Scope of AI in Healthcare
6.3 Responsible Artificial Intelligence (AI) in Dentistry
6.4 Explainable Artificial Intelligence (XAI) in Dentistry
6.5 Application of AI in Dentistry
6.6 Benefits of AI in Dentistry
6.7 Challenges of AI in Dentistry
6.8 Conclusion
References
7 Explainable Artificial Intelligence Technique in Deep Learning–Based Medical Image Analysis
7.1 Introduction
7.2 Deep Learning (DL) in the Analysis of Medical Images
7.3 Guidelines for Clinical XAI
7.4 Factors to Examine about the Feasibility and Efficacy of Using the Product in the Clinical Environment
7.5 Factors to Consider During the Evaluation
7.6 XAI in Medical Image Analysis
7.7 Non-Visual XAI Techniques in Medical Imaging
7.8 Challenges and Future Directions
7.9 Conclusion
References
8 XAI Technique in Deep Learning–Based Medical Image Analysis
8.1 Introduction
8.2 XAI Method in Field of Medical Imaging
8.3 Application of XAI in Medical Imaging
8.4 Conclusion
References
9 XAI-Enabled Telehealth
List of Abbreviations
9.1 Introduction
9.2 Significance of Telemedicine
9.3 Reasonable AI Consciousness (XAI)
9.4 Simulated Intelligence in Telemedicine
9.5 Challenges in Executing XAI in Medical Services
9.6 Clinical Choice Help
9.7 Patient Observing
9.8 Medical Services Intercessions
9.9 The Requirement for Mindful Simulated Intelligence in Medical Care
9.10 Moral Contemplations in Artificial Intelligence Sending
9.11 AI (ML) in Artificial Intelligence
9.12 Strategies for Interpretable AI Models
9.13 Layer-Wise Relevance Propagation
9.14 Local Interpretable Model-Agnostic Explanations
9.15 Partial Dependence Plots (PDPs)
9.16 Straight Forwardness in Artificial Intelligence Calculations
9.17 Difficulties of Reasonable Artificial Intelligence Logical
9.18 Consolidating Computer-Based Intelligence in Medical Services Conveyance
9.19 Functional Ramifications of XAI in Medical Services Reasonable [62]
9.20 Available XAI Besides the Costs of Logic
9.21 Conversation
9.22 Conclusion
References
10 Intelligent Algorithm for Seizure Alignment Using EEG Clustering with Special Reference to Discrete Wavelet Transform Theory
10.1 Introduction
10.2 Different Intelligent/Computational Approaches for Seizure Classification
10.3 The Architecture of EEG-Specific CNNs
10.4 Training EEG-Specific CNNs
10.5 Significance of EEG CNNs
10.6 Challenges and Future Directions
10.7 Recurrent Neural Networks
10.8 Applications in EEG Analysis
10.9 Ensemble Methods
10.10 Transfer Learning
10.11 Seizure EEG Clustering Using Discrete Wavelet Transform Algorithm
10.12 Present Findings
10.13 Conclusion
References
11 Analysis of Biomedical Data with Explainable (XAI) and Responsive AI (RAI)
Abbreviations
11.1 Introduction
11.2 Explainable Artificial Intelligence Modeling for Biomedical Data Analysis Using a Correlation-Based Feature Selection Method
11.3 Biomedical Data Analysis of Various Diseases: The Functions of XAI and RAI
11.4 A Comparative Study Between Manual Analysis and Analysis with XAI and RAI
11.5 Differentiation of AI and XAI/RAI Methods
11.6 Analyzing Data Using Traditional Methods Versus Using AI can Differ Significantly in Several Aspects
11.7 Advantages of AI
11.8 Comparison of AI’s Pros and Cons
11.9 Future Aspects
11.10 Conclusion
References
12 Classify Chronic Wounds: The Need of Explainable AI and Responsible AI
List of Abbreviation
12.1 Introduction
12.2 Understanding Chronic Wounds
12.3 The Rise of AI in Wound Classification
12.4 Explainable AI: Unravelling the Black Box
12.5 Responsible AI in Wound Classification
12.6 Case Studies and Applications
12.7 Conclusion
References
13 Bone Metastases: Explainable AI and Responsible AI
Abbreviations
13.1 Introduction to Bone Metastases
13.2 Traditional Diagnostic and Therapeutic Method for Bone Metastasis
13.3 AI Involvement in Diagnosis and Therapy of Bone Metastasis
13.4 Case Studies of Current AI Success in Bone Metastasis
13.5 Recent Advancements and Future Perspectives
13.6 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Cognitive neurostimulation techniques, their mechanisms, and associa...
Table 2.2 Explainable AI in cognitive neurostimulation.
Chapter 3
Table 3.1 Applications of XAI techniques (algorithms) [20–22].
Table 3.2 SHAP XAI algorithms discussion—literature [23, 24].
Table 3.3 Scope of surgery.
Table 3.4 Studies on automated robots.
Chapter 5
Table 5.1 Challenges and the development approaches to AI and ML in spinal car...
Table 5.2 RAI principles [97].
Chapter 7
Table 7.1 The guidelines for designing and assessing XAI. Assessment procedure...
Chapter 8
Table 8.1 Various XAI methods with their explanation.
Table 8.2 Application of XAI technique with their description.
Chapter 9
Table 9.1 Frequent terminology, definitions, and real-world illustrations empl...
Table 9.2 The main topics, supporting themes, and illustrative quotes that bes...
Chapter 10
Table 10.1 A confusion matrix of sensitivity and specificity of various subjec...
Chapter 11
Table 11.1 Advantages of XAI and RAI [25].
Chapter 13
Table 13.1 Different types of cancer and their incidence rate of bone metastas...
Table 13.2 Examples of AI tools available for bone metastasis.
List of Illustrations
Chapter 1
Figure 1.1 Landscapes of introduction split section.
Figure 1.2 The objectives of the chapter.
Figure 1.3 The flow of this chapter.
Figure 1.4 The applications of NLP in healthcare industry.
Figure 1.5 A collection of approaches and benefits of XAI in smart healthcare.
Figure 1.6 The patient engagement and care management in health sector.
Chapter 2
Figure 2.1 An overview of the recommended procedure is divided into two main p...
Figure 2.2 The use of neurostimulation computational modeling in neurological ...
Figure 2.3 Morally and responsibly generated and implemented artificial intell...
Chapter 4
Figure 4.1 Examining naturally transparent models as comparison to black box m...
Figure 4.2 Incorporating human knowledge into AI models can improve the qualit...
Figure 4.3 A diagram showing the fundamental steps and general architecture of...
Chapter 5
Figure 5.1 Spinal metastases of AI techniques.
Figure 5.2 The XAI techniques taxonomy.
Chapter 6
Figure 6.1 Medical diagnostic cycle.
Figure 6.2 Various application of AI in dentistry.
Figure 6.3 Application of artificial intelligence in endodontics.
Chapter 7
Figure 7.1 The proposed XAI framework.
Chapter 8
Figure 8.1 Concept and theory behind deep learning.
Figure 8.2 XAI-based medical image analysis flow diagram.
Figure 8.3 Various application of XAI in medical image analysis.
Chapter 9
Figure 9.1 Image analysis (AI vs. traditional techniques).
Figure 9.2 Different network of artificial intelligence.
Chapter 10
Figure 10.1 Flowchart shows various seizure classifications [45].
Figure 10.2 Block diagram of the proposed approach.
Figure 10.3 EEG signals extracted from three different subjects.
Figure 10.4 DWT decomposition using db3 level-3.
Figure 10.5 Processing block diagram with feed-forward back propagation neural...
Chapter 11
Figure 11.1 Basic functionalities of XAI and RAI in biomedical data analysis [...
Figure 11.2 Beneficiary aspects of XAI and RAI in healthcare [26].
Figure 11.3 Challenges faced in the usage of AI and RAI [27].
Chapter 13
Figure 13.1 Certain niches in the bone marrow are colonized by tumor cells. Mo...
Figure 13.2 Classification of traditional diagnostic methods.
Figure 13.3 Classification of traditional systemic treatments.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Foreword
Preface
Begin Reading
Index
Wiley End User License Agreement
Pages
ii
iii
iv
xix
xxi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Explainable and Responsible Artificial Intelligence in Healthcare
Edited by
Rishabha Malviya
Department of Pharmacy School of Medical and Allied Sciences, Galgotias University
and
Sonali Sundram
Department of Pharmacy School of Medical and Allied Sciences, Galgotias University
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.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-30241-3
Cover image: Adobe FireflyCover design by Russell Richardson
Foreword
The application of artificial intelligence (AI) in healthcare has enormous potential for shaping patient care, enhancing diagnosis precision, and advancing medical research. However, it remains essential that AI technologies are effective, reliable, transparent, and ethical. This book is a thorough assessment of these critical themes, covering topics such as XAI-based reinforcement learning in smart healthcare systems, diagnostic and surgical applications of explainable AI, biomedical data analysis, and chronic wound classification.
One of the most impressive elements of this book is its unwavering commitment to promoting transparency and accountability in AI-powered healthcare solutions. By suggesting explainable and responsible AI applications, the authors not only increase trust in these technologies but also establish the framework for their ethical and long-term inclusion into clinical practice. To promote applications of AI, I believe this book will be an inspiration that will guide the ethical progress of AI in healthcare.
Dr. Rishabha Malviya meticulously compiled this volume and explored the AI applications in various medical disciplines, providing insightful case studies and scholarly analysis, highlighting the transformative potential of AI while also addressing ethical considerations. Each chapter offers a nuanced exploration of AI applications in various medical disciplines, from neurology to dentistry, and diagnostic imaging to telehealth.
Best wishes and happy reading.
Dr. Dhruv GalgotiaCEO
Galgotias University, Greater Noida
Preface
The application of artificial intelligence (AI) in healthcare has the potential to transform patient care, diagnosis, and treatment. However, such systems must be efficient, transparent, responsible, and ethically sound. This book is an in-depth study of the relationship between AI approaches and the complicated fields of contemporary healthcare.
The book explores the significance of explainable and responsible AI applications in various medical fields, beginning with an in-depth analysis of XAI-based reinforcement learning and natural language processing in smart healthcare systems. It then proceeds to explore XAI and RAI uses in neuroscience, diagnostic imaging, surgical planning, and other fields. Each chapter provides a distinct viewpoint on how XAI and RAI approaches can help healthcare practitioners increase diagnosis accuracy, optimize treatment plans, and improve patient outcomes. The editors and writers are enthusiastic about AI’s potential to drive positive change in healthcare, but they also acknowledge the ethical, legal, and societal ramifications of AI integration. The book builds upon diverse perspectives while also providing readers with the knowledge and skills they need to appropriately navigate the complicated field of healthcare-based AI.
We believe that this book will be a significant resource for healthcare practitioners, scientists, administrators, and every individual interested in using AI to improve human health. We are grateful to the contributors who contributed, as well as the readers who joined us on this journey. Finally, the editors thank Martin Scrivener and Scrivener Publishing for their assistance and publication of this book.
The EditorsDecember 2024
1Uncapping Explainable Artificial Intelligence–Centered Reinforcement Learning and Natural Language Processing in Smart Healthcare System
Bhupinder Singh1*, Rishabha Malviya2, Christian Kaunert3,4 and Sathvik Belagodu Sridhar5
1School of Law, Sharda University, Greater Noida, India
2Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
3School of Law and Government, Dublin City University, Dublin, Ireland
4Director of the International Centre for Policing and Security at the University of South Wales, Cardiff, UK
5Department of Clinical Pharmacy and Pharmacology, RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates
Abstract
Smart healthcare involves utilizing technologies like cloud computing, Internet of Things (IoT), and artificial intelligence (AI) to establish an effective, convenient, and tailored healthcare system. Health data gathered at a user level can be shared with healthcare professionals for further evaluation. AI, combined with this data, aids in health screening, early disease detection, and treatment planning. Explainable AI (XAI) is a key component of the responsible AI approach and is a prerequisite for the responsible application of AI. The goal of XAI’s approach is to investigate several approaches in order to provide a variety of strategies that will provide future developers with a range of design options that address the trade-off between explainability and performance. Natural language processing (NLP) techniques are revolutionizing electronic health record (EHR) management within the healthcare sector. Utilizing recurrent neural network models, NLP methods are employed to analyze unstructured EHR notes, extracting clinical information such as signs and symptoms represented by named entities. This chapter comprehensively explores the diverse arena of XAI-based reinforcement learning in smart healthcare sector with NLP.
Keywords: Explainable artificial intelligence (XAI), healthcare, natural language processing techniques, patient engagement, clinical practices
1.1 Introduction
The term “smart healthcare” refers to a framework for the delivery of healthcare that makes use of cutting-edge technologies such as big data, blockchain, artificial intelligence (AI), cloud/edge computing, and the Internet of Things (IoT) to create a variety of intelligent systems that connect healthcare stakeholders and improve the standard of care [1, 2]. These applications include medical picture analysis, cancer-related sickness analysis, patient health data recording, and relevant economic data [3]. Historically, cloud-based AI learning and data analytics capabilities have been the main focus of smart healthcare systems [4, 5]. However, because raw data transfer results in inefficient communication delay, this centralized strategy is unable to attain significant network scalability [6, 7]. Regulations like the US Health Insurance Portability and Accountability Act apply to personal data in e-healthcare. Also, a dispersed AI system over a broad Internet-of-Medical-Things (IoMT) network may be required if a centralized AI system is not found to be feasible in future healthcare systems [8]. As such, moving to distributed AI techniques at the network edge is essential for intelligent healthcare systems that are both scalable and privacy-preserving [9].
Smart healthcare involves utilizing technologies like cloud computing, IoT, and AI to establish an effective, convenient, and tailored healthcare system [10]. These technologies enable real-time health monitoring through healthcare apps on smartphones or wearables, empowering individuals to manage their well-being [11]. Health data gathered at a user level can be shared with healthcare professionals for further evaluation. AI, combined with this data, aids in health screening, early disease detection, and treatment planning [12]. However, in healthcare, the ethical dilemma of transparency concerning AI and the skepticism surrounding the opaque nature of AI systems necessitates the development of AI models that can be elucidated [13]. These AI methods, geared toward explaining AI models and their predictions, are referred to as explainable AI (XAI) techniques [14].
Figure 1.1 Landscapes of introduction split section.
1.1.1 XAI in Healthcare: Relevance and Overview
The emergence of the IoMT has led to notable changes in the way healthcare institutions operate, improving the quality of care that they offer [14, 15]. The IoMT devices are frequently used to gather healthcare data because they are capable of sensing and transmitting updates on an individual’s health [15, 16]. Artificial intelligence is then employed to process this data, leading to a variety of healthcare applications, such as illness prognosis and remote patient monitoring [17].
1.1.2 Importance of Explainability in AI
Explainable AI is a key component of the responsible AI approach and is a prerequisite for the responsible application of AI [18, 19]. This method emphasizes justice, model explainability, and accountability while promoting the broad use of AI approaches in actual corporate contexts [20, 21]. The organizations must build AI systems based on trust and transparency by incorporating ethical standards into AI applications and procedures in order to encourage the responsible deployment of AI [22, 23].
1.1.3 Role of Reinforcement Learning and NLP in Smart Healthcare
With new AI technologies enabling a variety of intelligent applications across diverse healthcare contexts, smart healthcare has advanced significantly [24, 25]. Because it can understand and analyze human language, natural language processing (NLP) is one of these technologies that stand out as being essential [26]. In this work, it does a thorough analysis of previous studies on the application of NLP in smart healthcare, looking at both its theoretical underpinnings and real-world uses [27, 28].
Modern technologies have long been adopted by the healthcare industry, where machine learning (ML) and AI have many uses similar to those in business and e-commerce. With this technology, the possibilities are practically endless. With its creative uses, ML is significantly changing the healthcare sector [8]. Driven by prerequisites like electronic medical records (EMR), healthcare institutions have already incorporated next-generation data analytics using big data techniques. With ML technologies, this process may be further improved, leading to higher-quality automation and more intelligent decision-making in public healthcare systems and primary/tertiary patient care [29]. This has the potential to significantly enhance the standard of living for billions of people around the globe. Personalized medicine, treatment planning, scheduling surgeries and appointments, and other complex medical decision-making problems have found a strong advocate in reinforcement learning (RL) [30]. Within the field of NLP, RL has attracted a lot of attention due to its ability to learn the best possible methods for tasks such as question answering, machine translation, and conversation systems [31].
1.1.4 Objectives of the Chapter
This chapter has the following objectives:
to provide a contemporary overview of federated learning (FL)–based AI in healthcare, starting with fundamental concepts of FL and key AI principles, alongside features of XAI, followed by an in-depth exploration of the efficiency of smart healthcare;
to introduce recent advancements in FL-XAI taxonomies and emerging integrations of AI-FL, highlighting their relevance to intelligent healthcare applications;
to tackle technical challenges existing within current systems and propose solutions utilizing a wide array of technologies, including FL, AI, and XAI, within healthcare applications; and
to scrutinize the challenges posed by various applications and chart a path for further research focusing on XAI in innovative healthcare domains.
Figure 1.2 The objectives of the chapter.
1.1.5 Structure of the Chapter
This chapter comprehensively explores the XAI-based RL and NLP in smart healthcare system. Section 1.2 expresses the XAI-based RL in smart healthcare systems. Section 1.3 discusses the NLP in smart healthcare systems. Section 1.4 specifies the incorporation of XAI-based RL and NLP. Section 1.5 highlights the synergies between XAI, RL, and NLP in healthcare. Section 1.6 elaborates the patient engagement and care management in health sector: XAI and NLP methods. Finally, Section 1.7 concludes the chapter, as well as the future scope and implications for healthcare practice.
1.2 XAI-Based Reinforcement Learning in Smart Healthcare Systems
Reinforcement learning (RL) has become a potent method for addressing intricate medical decision-making challenges, including treatment planning, personalized medicine, and optimizing surgery and appointment schedules [32]. However, the opaque nature of AI based on deep learning algorithms makes physicians unsure about the results of their diagnoses [33, 34]. Providing strong proof of these results is difficult because AI models and human understanding differ, a phenomenon known as “blackbox” transparency [35, 36]. A lot of research efforts are focused on making AI models more understandable so that physicians may feel more confident about using them. For example, XAI was first proposed by the US Defense Advanced Research Projects Agency in 2015. The utility of XAI in multidisciplinary fields like computer science, statistics, and psychology was subsequently shown by a trust AI project in 2021, indicating that the explanations offered by XAI may improve user trust [37, 38].
Figure 1.3 The flow of this chapter.
Techniques such as XAI can explain their own behavior, pointing out its advantages and disadvantages as well as offering predictions about how it will behave in the future [39, 40]. The goal of XAI’s approach is to investigate several approaches in order to provide a variety of strategies that will provide future developers with a range of design options that address the trade-off between explainability and performance [8, 41]. Expert system explanations go beyond summarizing the behaviors of the system to explain the reasons behind them, which requires knowledge of the system’s design and building process [42, 43].
The architecture of explainable expert systems (EES) was developed to capture the design components required to produce thorough explanations [44]. EES represents both the underlying ideas that guided the creation of the system and the execution of those concepts with a focus on capturing how a general principle was implemented inside a particular domain or scenario [45]. In order to provide useful explanations, a system has to learn more and provide its explanations in a responsive and adaptable way [46]. It must understand how its justifications were constructed in order to offer further detail or clarity as necessary. Users find it difficult to understand the activities of computer-controlled entities in military simulations due to the growing complexity of AI systems and behavioral models [47]. In order to explain the behavior of simulated entities, a number of research have suggested new modular and domain-independent designs that include elements like the ability to explain the reasoning behind entity behaviors and modularity to make interaction with other components easier [48].
1.3 Natural Language Processing in Smart Healthcare Systems
The goal of the computer science and AI field of NLP is to automatically analyze, represent, and interpret human language [49, 50]. In recent years, NLP has become a vibrant field of study, receiving substantial interest from a range of research communities. Natural language processing (NLP) is essential to smart healthcare because it allows robots to comprehend and communicate with humans through the use of human language, which is the fundamental mode of communication for intelligent systems. Text and speech are the two main ways that human language is expressed. Text records, articles, dialogues, and more may all be examples of this [51].
Since its first development in AI in the 1950s, NLP has been evolving for several decades. Three primary categories may be used to classify approaches to NLP: rule-based, statistical, and deep learning–based techniques [52]. In the early years of NLP, from the 1950s to the 1980s, most research was done using rule-based methods, in which linguists and computer scientists created rules that took into account human language. But even well-crafted rules have coverage gaps because of human language’s flexibility and complexity [53]. Statistical NLP systems have been more and more popular since the 1980s. These systems use statistical and ML techniques to extract features from big datasets, or corpora. Their better performance and durability led to their eventual replacement of rule-based NLP systems [54]. Neural NLP has gained prominence from the early days of neural probabilistic language models and the subsequent fast progress in deep learning after 2013. Neural network NLP has become the dominating method in current research, achieving state-of-the-art performance in many NLP tasks by utilizing neural networks and large corpora for automated feature learning [55].
Propelled by growing AI technologies that enable numerous intelligent applications across multiple healthcare contexts, smart healthcare has achieved considerable progress [56, 57]. Because it can understand and analyze human language, NLP is one of these technologies that stand out as being extremely important [60]. This work performs a thorough analysis of previous research on NLP in smart healthcare, looking at both theoretical approaches and real-world implementations [58]. From a technical standpoint, the evaluation mostly focuses on feature extraction and modeling for a variety of NLP tasks found in smart healthcare [59]. The conversation around NLP-based smart healthcare applications mostly focuses on standard cases including clinical practice, hospital administration, individual care, public health campaigns, and medication research [61].
1.3.1 Applications of NLP in Healthcare Industry
1.3.1.1 Deidentification of Clinical Records
Natural language processing (NLP) techniques are revolutionizing electronic health record (EHR) management within the healthcare sector [62]. Utilizing recurrent neural network models, NLP methods are employed to analyze unstructured EHR notes, extracting clinical information such as signs and symptoms represented by named entities [63]. Besides, NLP systems are utilized to extract data from EMRs containing clinical free text, with the objective of integrating top-notch annotators [64]. The rule-based learning is effectively employed for the deidentification of clinical records, representing a typical application in medical informatics. Also, assistive diagnostic systems have been developed, enabling simultaneous disease diagnosis and generation of corresponding syndromes [65].
1.3.1.2 Medical Text Mining
The next important use of NLP in the healthcare industry is health text mining [66]. Notably, models based on deep learning have been created to replicate the distinction of lung cancer syndrome [67, 68]. These models use data gathered from actual treatment cases by lung cancer specialists from unstructured medical records as input [69]. End-to-end models are being developed to improve medical record output and enable lung cancer therapy [70]. Hiring the right target population is crucial for accurately classifying documents that describe clinical trial eligibility requirements [71]. These attempts are being made to develop ensemble learning techniques that combine deep learning models, like BERT, ERNIE, XLNet, and RoBERT using the advantages of several models to provide results that are more accurate and consistent [72].
1.3.1.3 Extraction of Medical Information
In biomedical text mining, automated connection extraction between substances and illnesses is essential [73, 74]. Narratives that describe the course of a condition, prescriptions, treatments, surgical operations or discharge summaries frequently contain temporal information [75]. Clinical research and practice may be advanced by the extraction and standardization of temporal expressions, which can greatly improve narrative clinical text analysis and understanding [76]. Also, current research investigates the use of NLP approaches to extract family history information from synthetic clinical narratives [77, 78].
1.3.1.4 Text Data Management
Textual data, encompassing diagnosis records, operation records, discharge summaries, eligibility criteria of clinical trials, social media comments, online health discussions, and medical publications, is abundant within the medical domain in an unstructured format [79]. NLP techniques offer valuable assistance in addressing this information overload by aggregating and summarizing patient notes, analyzing treatment data, extracting and retrieving information from extensive discharge summaries, and comprehending the semantic nuances of patient inquiries [80, 81]. NLP can aid in medical decision-making by automatically analyzing patterns within large volumes of textual data, identifying commonalities and differences, and suggesting appropriate actions on behalf of domain experts [82, 83].
1.3.1.5 Health Information Graph
Health insurance is another big obstacle in the field of medicine, especially when it comes to making sure that prescription drugs are clinically suitable for the patient’s ailment [84]. This is a critical step in the detection of fraud, waste, and abuse [85]. Techniques for deep learning provide an answer to this problem [86, 87]. A multilevel similarity-matching approach has been devised for entity linking, extracting entities and connections from information sources using health knowledge graph [88]. The issues with text representation, extraction speed, and knowledge graph size limitations still exist. It is expected that these issues will be resolved as ML methods advance over time [89].
Figure 1.4 The applications of NLP in healthcare industry.
1.4 Incorporation of XAI-Based RL and NLP
The wide ranges of applications for ML technology are available to improve clinical trial research [90, 91]. Medical practitioners may evaluate a wider range of data and cut down on the expense and duration of examinations by using advanced predictive analytics on clinical trial candidates [92]. Clinical trial efficiency may be further improved by a variety of ML applications [93, 94]. ML can help determine the best sample sizes for increased efficacy and reduce data inaccuracies by utilizing EHRs [95]. This tackles a major issue in the healthcare sector because there are less and fewer highly qualified radiologists in the world [96]. The ability to provide more dynamic and effective tailored medicines by fusing personal health data with predictive analytics is another benefit of ML in the healthcare industry. There is a lot of promise for ML research and clinical trials [97]. Predictive research powered by ML may help identify latent clinical trial volunteers from a variety of data sources, such as past medical visits and social media activity [98]. While ML algorithms can scan electronically recorded medical imaging data and find abnormalities and patterns equivalent to highly competent radiologists, ML technologies may also be used to monitor trial participants in real-time and expedite trial administration operations [99, 100]. As a result, it is expected that the use of these platforms to assist radiologists will increase [101].
Real-time trial participant observation can further enhance trial management, and ML-based predictive analytics can help researchers quickly discover possible clinical trial volunteers from a variety of data sources [102]. In this case, ML can help researchers figure out the best sample size to test and reduce database inaccuracies using electronic records. This paper’s main goal is to investigate ML’s enormous potential in the medical field [103]. Although ML model developments remain a major driver of NLP’s success, the field has great potential for the changing healthcare sector [104]. Data availability is one of the main issues because it might be challenging to get a fair sample of objective health data because of worries about data privacy and preservation. The issue is further compounded by the variability of the few data that is currently accessible [105, 106]. The dialects, linguistic variances, and specialist terminology provide difficulties for NLP algorithms in correctly comprehending the information that is currently accessible. In light of this, it may be said that NLP is still a developing field with a lot of unrealized promise [107].
1.5 Synergies Between XAI, RL, and NLP in Healthcare
Artificial intelligence is revolutionizing the healthcare industry in a number of ways [98]. In order to successfully handle the physical and emotional issues that the population faces, this discipline is subjected to considerable study with the goal of enhancing overall healthcare development [99]. Such method in AI that offers a variety of skills in areas like data management, clinical trials, and well-informed medical decision-making is NLP, a deep learning technology [100]. A multitude of models have been created, exhibiting encouraging results concerning improving data administration and fostering mental and physical health [101].
The term “explainable AI” (XAI) refers to a collection of approaches designed to make AI systems comprehensible and visible and the typical methods include some of the following.
1.5.1 Rule-Based Systems
These systems base their choices on a predetermined set of clear guidelines or rational presumptions. It is not too difficult to grasp how the system makes decisions because the rules are clear and easy to interpret [102].
1.5.2 Bayesian Networks
These systems depict links between several variables by using probabilistic models [103]. They aid in clarifying the reasoning behind certain actions by assessing the likelihood of possible outcomes [104].
1.5.3 Decision Trees
These systems show various decision routes with a structure like a tree. Every node in the tree represents a choice, and the branches stand in for possible results [105]. This hierarchical structure makes the decision-making process easier to understand [106].
1.5.4 Deep Learning Models
Although they are the most complex kind of AI system, methods like layer-wise relevance propagation and attention processes can make deep learning models easier to understand [107, 108].
Depending on the type of AI system and the issue that it aims to solve, a particular XAI strategy may be chosen [109]. The following are several benefits of using XAI in healthcare.
1.5.5 Enhanced Trust
Patients and healthcare practitioners can cultivate a higher level of confidence in AI systems by providing comprehensible and transparent explanations for AI judgments [110].
1.5.6 Improved Understanding
XAI helps medical professionals understand how to use AI to make decisions about patient care, which raises the standard of care as a whole [111].
Figure 1.5 A collection of approaches and benefits of XAI in smart healthcare.
1.5.7 Reduced Biases
XAI makes it easier to identify and correct biases in AI systems, guaranteeing that patients receive fair and unbiased treatment [112].
1.5.8 Enhanced Adherence to Regulatory Standards
XAI could be necessary to comply with legal requirements, as those outlined in the General Data Protection Regulation of the European Union [113].
1.6 Patient Engagement and Care Management in Health Sector: XAI and NLP Methods
The patient engagement of XAI has completely changed the healthcare industry [114, 115]. In order to investigate the role of AI in healthcare, this thoroughly reviews the literature, concentrating on a number of important areas, including virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and other administrative applications [116]. The effects of AI can be seen in many domains such as the early diagnosis of clinical conditions using medical imaging, the management of EHRs, the discovery of new drugs and vaccines, the identification of medical prescription errors, the provision of virtual patient care using AI-driven tools, the improvement of patient engagement and adherence to treatment plans, the reduction of administrative burden on healthcare professionals, and the facilitation of technology-assisted rehabilitation [117, 118].
While medical coding uses NLP to give appropriate codes to medical procedures and diagnoses, clinical documentation uses NLP to automate the writing of patient information [119]. NLP is used in clinical decision support to evaluate patient data and deliver treatment suggestions to medical professionals; it is also used in patient engagement to enhance patient engagement and communication [120]. The application of NLP in the medical field dates back to the 1970s. At first, NLP was mostly used to automate transcription of medical records. Clinical trials, disease surveillance, and sentiment analysis are examples of NLP’s public health applications in healthcare [121, 122]. Sentiment analysis uses NLP to examine social media and other internet sources in order to understand public attitudes and viewpoints on health-related topics. NLP is used in clinical trials to find eligible people and examine trial information [123]. NLP is used in disease surveillance to keep an eye on and track disease trends and outbreaks [124]. But as NLP has developed so too have its usefulness in healthcare, now including a wider range of complex applications [125]. This explores the many uses of NLP in the medical field, dividing them into two primary areas: public health applications and clinical applications [126]. The goal of clinical NLP applications in healthcare is to improve patient care by providing medical professionals with the knowledge and resources that they need to make well-informed decisions about patient treatment [127]. Clinical documentation, medical coding, clinical decision support, and patient engagement are some of these uses [128].
Figure 1.6 The patient engagement and care management in health sector.
Numerous technological, moral, and social issues arise when AI is integrated into healthcare [129]. These issues include privacy concerns, safety concerns, the freedom to experiment and make decisions, financial implications, information transparency and consent, availability of AI-driven healthcare solutions, and the efficacy of AI interventions [130, 131]. In order to guarantee patient safety, accountability, and confidence among healthcare professionals, effective governance of AI applications is crucial [132]. This will increase adoption and provide notable health effects. Precise governance structures are required to support the adoption and application of AI in healthcare while addressing challenges relating to trust, ethics, and regulations [133]. Following the pandemic, AI has emerged, bringing about a revolutionary change in the healthcare industry and perhaps leading the way in meeting future healthcare demands [134].
1.7 Conclusion and Future Scope—Implications for Healthcare Practice
The term “smart healthcare” refers to a framework for the delivery of healthcare that makes use of cutting-edge technologies such as big data, blockchain, AI, cloud/edge computing, and the IoT to create a variety of intelligent systems that connect healthcare stakeholders and improve the standard of care. The public at large, healthcare service providers, and third-party healthcare companies are the three main groups of stakeholders in smart healthcare. These parties are engaged in a range of smart healthcare scenarios, such as smart hospitals and homes, health management, public health campaigns, advanced life sciences research and development, and rehabilitation therapy, among other things. Within the smart healthcare framework, Figure 1.1 shows the key players, new technology, and illustrative situations. As AI in healthcare continues to progress explainability and openness in AI decision-making become increasingly crucial.
References
1. Sasaki, N., Rosenberg, M., Shin, J.H., Kunisawa, S., Imanaka, Y., Hidden Populations for Healthcare Financial Protection in the Super-Aging Society: Closing the Gap Between Policy and Practice.
Clin. Soc. Work J.
, 1, 1–12, 2024.
2. Singh, B., Kaunert, C., Vig, K., Reinventing Influence of Artificial Intelligence (AI) on Digital Consumer Lensing Transforming Consumer Recommendation Model: Exploring Stimulus Artificial Intelligence on Consumer Shopping Decisions, in:
AI Impacts in Digital Consumer Behavior
, T. Musiolik, R. Rodriguez, H. Kannan (Eds.), pp. 141–169, IGI Global, USA, 2024,
https://doi.org/10.4018/979-8-3693-1918-5.ch006
.
3. Singh, B., Featuring Consumer Choices of Consumable Products for Health Benefits: Evolving Issues from Tort and Product Liabilities.
J. Law Torts Consum. Prot. Law
,
7
, 1, 15, 2024.
4. Gostin, L.O., A framework convention on global health: health for all, justice for all.
JAMA
,
307
, 19, 2087–2092, 2024.
5. Tulenko, K., Mgedal, S., Afzal, M.M., Frymus, D., Oshin, A., Pate, M., Zodpey, S., Community health workers for universal health-care coverage: from fragmentation to synergy.
Bull. World Health Organ.
,
91
, 847–852, 2023.
6. World Health Organization,
Delivering global health security through sustainable financing: 26-27 July 2017, Seoul, Republic of Korea
(No. WHO/WHE/CPI/2018.38), World Health Organization, 2018.
7. Hill, P.S., Understanding global health governance as a complex adaptive system.
Global Public Health
,
6
, 6, 593–605, 2021.
8. Singh, B., Blockchain Technology in Renovating Healthcare: Legal and Future Perspectives, in:
Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
, pp. 177–186, IGI Global, USA, 2023.
9. World Health Organization, Everybody’s business–strengthening health systems to improve health outcomes: WHO’s framework for action, 2007.
10. Singh, B., Unleashing Alternative Dispute Resolution (ADR) in Resolving Complex Legal-Technical Issues Arising in Cyberspace Lensing E-Commerce and Intellectual Property: Proliferation of E-Commerce Digital Economy.
Rev. Bras. Altern. Dispute Resolut.-Braz. J. Altern. Dispute Resolut.-RBADR
,
5
, 10, 81–105, 2023.
11. Singh, B. and Kaunert, C., Integration of Cutting-Edge Technologies such as Internet of Things (IoT) and 5G in Health Monitoring Systems: A Comprehensive Legal Analysis and Futuristic Outcomes.
GLS Law J.
,
6
, 1, 13–20, 2024.
12. Kickbusch, I., Hein, W., Silberschmidt, G., Addressing global health governance challenges through a new mechanism: the proposal for a Committee C of the World Health Assembly.
J. Law Med. Ethics
,
38
, 3, 550–563, 2024.
13. World Health Organization, Decade of healthy ageing: baseline report, WHO, Japan, 2020.
14. Gostin, L.O., Habibi, R., Meier, B.M., Has global health law risen to meet the COVID-19 challenge? Revisiting the International Health Regulations to prepare for future threats.
J. Law Med. Ethics
,
48
, 2, 376–381, 2020.
15. Bodeker, G. and Kronenberg, F., A public health agenda for traditional, complementary, and alternative medicine.
Am. J. Public Health
,
92
, 10, 1582–1591, 2022.
16. Bodeker, G.,
WHO global atlas of traditional, complementary, and alternative medicine
, vol. 1, World Health Organization, Japan, 2023.
17. Magnusson, R.S., Rethinking global health challenges: towards a ‘global compact’ for reducing the burden of chronic disease.
Public Health
,
123
, 3, 265–274, 2019.
18. Singh, B., Green Infrastructure in Real Estate Landscapes: Pillars of Sustainable Development and Vision for Tomorrow.
Natl. J. Real Estate Law
,
7
, 1, 4–8, 2024.
19. Singh, B., Tele-Health Monitoring Lensing Deep Neural Learning Structure: Ambient Patient Wellness via Wearable Devices for Real-Time Alerts and Interventions.
Indian J. Health Med. Law
,
6
, 2, 12–16, 2023.
20. World Health Organization, Global strategy on human resources for health: workforce 2030, 2016.
21. Gostin, L.O., Sridhar, D., Hougendobler, D., The normative authority of the World Health Organization.
Public Health
,
129
, 7, 854–863, 2024.
22. World Health Organization,
Global action plan for the prevention and control of noncommunicable diseases 2013-2020
, World Health Organization, 2013.
23. World Health Organization,
Promoting mental health: Concepts, emerging evidence, practice: Summary report
, World Health Organization, 2004.
24. Singh, B., Cherish Growth, Advancement and Tax Structure: Addressing Social and Economic Prospects.
J. Taxation Regul. Framework
,
7
, 1, 7–10, 2024.
25. Singh, B., Legal Dynamics Lensing Metaverse Crafted for Videogame Industry and E-Sports: Phenomenological Exploration Catalyst Complexity and Future.
J. Intellect. Prop. Rights Law
,
7
, 1, 8–14, 2024.
26. Fortanier, F., Kolk, A., Pinkse, J., Harmonization in CSR reporting: MNEs and global CSR standards.
Manage. Int. Rev.
,
51
, 665–696, 2021.
27. Meier, B.M., Cinà, M.M., Gostin, L.O., Advancing human rights through global health governance, in:
Foundations of global health and human rights
, 2020.
28. Van Lerberghe, W.,
The world health report 2008: primary health care: now more than ever
, World Health Organization, Japan, 2018.
29. Singh, B., Federated Learning for Envision Future Trajectory Smart Transport System for Climate Preservation and Smart Green Planet: Insights into Global Governance and SDG-9 (Industry, Innovation and Infrastructure).
Natl. J. Environ. Law
,
6
, 2, 6–17, 2023.
30. Sharma, A. and Singh, B., Measuring Impact of E-commerce on Small Scale Business: A Systematic Review.
J. Corp. Gov. Int. Bus. Law
,
5
, 1, 45, 2022.
31. Lachat, C., Van Camp, J., De Henauw, S., Matthys, C., Larondelle, Y., Remaut–De Winter, A.M., Kolsteren, P., A concise overview of national nutrition action plans in the European Union Member States.
Public Health Nutr.
,
8
, 3, 266–274, 2023.
32. Gostin, L.O. and Sridhar, D., Global health and the law.
N. Engl. J. Med.
,
370
, 18, 1732–1740, 2024.
33. Marcus, M., Yasamy, M.T., van Ommeren, M.V., Chisholm, D., Saxena, S., Depression: A global public health concern, 2012.
34. Al-Shaar, L., Vercammen, K., Lu, C., Richardson, S., Tamez, M., Mattei, J., Health effects and public health concerns of energy drink consumption in the United States: a mini-review.
Front. Public Health
,
5
, 286776, 2017.
35. Gostin, L.O., A framework convention on global health: Health for all, justice for all.
JAMA
,
307
, 19, 2087–2092, 2022.
36. Singh, B., Relevance of Agriculture-Nutrition Linkage for Human Healthcare: A Conceptual Legal Framework of Implication and Pathways.
Justice Law Bull.
,
1
, 1, 44–49, 2022.
37. Singh, B., COVID-19 Pandemic and Public Healthcare: Endless Downward Spiral or Solution via Rapid Legal and Health Services Implementation with Patient Monitoring Program.
Justice Law Bull.
,
1
, 1, 1–7, 2022.
38. Singh, B., Global science and jurisprudential approach concerning healthcare and illness.
Indian J. Health Med. Law
,
3
, 1, 7–13, 2020.
39. Singh, B., Profiling Public Healthcare: A Comparative Analysis Based on the Multidimensional Healthcare Management and Legal Approach.
Indian J. Health Med. Law
,
2
, 2, 1–5, 2019.
40. Kickbusch, I., Brindley, C., World Health Organization, Health in the post-2015 development agenda: an analysis of the UN-led thematic consultations, High-Level Panel report and sustainable development debate in the context of health. World Health Organization, 2023.
41. Jebril, N., World Health Organization declared a pandemic public health menace: a systematic review of the coronavirus disease 2019 “COVID-19”.
Int. J. Psychosoc. Rehabil.
, 24, 9, 2020, Available at SSRN 3566298.
42. Singh, B., Blockchain Technology in Renovating Healthcare: Legal and Future Perspectives, in:
Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
, pp. 177–186, IGI Global, USA, 2023.
43. Callaway, M.V., Connor, S.R., Foley, K.M., World Health Organization public health model: a roadmap for palliative care development.
J. Pain Symptom Manage.
,
55
, 2, S6–S13, 2018.
44. Drager, N. and Sunderland, L., Public health in a globalising world: The perspective from the World Health Organization, in:
Governing Global Health
, pp. 67–78, Routledge, USA, 2024.
45. World Health Organization,
A global brief on hypertension: Silent killer, global public health crisis: World Health Day 2013
(No. WHO/DCO/WHD/2013.2), World Health Organization, 2013.
46. Cueto, M., Brown, T.M., Fee, E.,
The world health organization: A history
, Cambridge University Press, USA, 2019.
47. World Health Organization, Essential public health functions, health systems and health security: developing conceptual clarity and a WHO roadmap for action, 2018.
48. Buliva, E., Elhakim, M., Tran Minh, N.N., Elkholy, A., Mala, P., Abubakar, A., Malik, S.M.M.R., Emerging and reemerging diseases in the World Health Organization (WHO) Eastern Mediterranean Region—progress, challenges, and WHO initiatives.
Front. Public Health
,
5
, 276, 2017.
49. Talebi Bezmin Abadi, A., Rizvanov, A.A., Haertlé, T., Blatt, N.L., World Health Organization report: current crisis of antibiotic resistance.
BioNanoScience
,
9
, 778–788, 2019.
50. Lopes, M.B.S., The 2017 World Health Organization classification of tumors of the pituitary gland: a summary.
Acta Neuropathol.
,
134
, 521–535, 2017.
51. World Health Organization,
Communicating risk in public health emergencies: A WHO guideline for emergency risk communication (ERC) policy and practice
, World Health Organization, 2017.
52. McCoy, D., Kapilashrami, A., Kumar, R., Rhule, E., Khosla, R., Developing an agenda for the decolonization of global health.
Bull. World Health Organ.
,
102
, 2, 130, 2024.
53. World Health Organization,
World report on ageing and health
, World Health Organization, 2015.
54. Schneider, M.J.,
Introduction to public health
, Jones & Bartlett Learning, USA, 2020.
55. Folland, S., Goodman, A.C., Stano, M.,
The economics of health and health care: Pearson new international edition
, Routledge, USA, 2024.
56. Igwaran, A. and Okoh, A.I., Human campylobacteriosis: A public health concern of global importance.
Heliyon
,
5
, 11, 12, 2019.
57. Ji, X., Chun, S.A., Wei, Z., Geller, J., Twitter sentiment classification for measuring public health concerns.
Soc. Netw. Anal. Min.
,
5
, 1–25, 2023.
58. Leen, J.L. and Juurlink, D.N., Carfentanil: a narrative review of its pharmacology and public health concerns.
Can. J. Anaesth.
,
66
, 4, 414–421, 2019.
59. Sanyaolu, A., Okorie, C., Qi, X., Locke, J., Rehman, S., Childhood and adolescent obesity in the United States: a public health concern.
Glob. Pediatr. Health
,
6
, 2333794X19891305, 2019.
60. Shaw, F.E., Asomugha, C.N., Conway, P.H., Rein, A.S., The Patient Protection and Affordable Care Act: opportunities for prevention and public health.
Lancet
,
384
, 9937, 75–82, 2024.
61. Eger, H., Chacko, S., El-Gamal, S., Gerlinger, T., Kaasch, A., Meudec, M., Uribe, O.L., Towards a Feminist Global Health Policy: Power, intersectionality, and transformation.
PLOS Global Public Health
,
4
, 3, e0002959, 2024.
62. Erlangga, D., Suhrcke, M., Ali, S., Bloor, K., The impact of public health insurance on health care utilisation, financial protection and health status in low-and middle-income countries: a systematic review.
PLoS One
,
14
, 8, e0219731, 2019.
63. Chersich, M.F., Gray, G., Fairlie, L., Eichbaum, Q., Mayhew, S., Allwood, B., Rees, H., COVID-19 in Africa: care and protection for frontline healthcare workers.
Global. Health
,
16
, 1–6, 2020.
64. Dewa, C.S., Loong, D., Bonato, S., Trojanowski, L., The relationship between physician burnout and quality of healthcare in terms of safety and acceptability: a systematic review.
BMJ Open
,
7
, 6, 23, 2017.
65. Batalden, M., Batalden, P., Margolis, P., Seid, M., Armstrong, G., Opipari-Arrigan, L., Hartung, H., Coproduction of healthcare service.
BMJ Qual. Saf.
, 1, 88, 2023.
66. Balarajan, Y., Selvaraj, S., Subramanian, S.V., Health care and equity in India.
Lancet
,
377
, 9764, 505–515, 2021.
67. Godman, B., Haque, M., Islam, S., Iqbal, S., Urmi, U.L., Kamal, Z.M., Sefah, I., Rapid assessment of price instability and paucity of medicines and protection for COVID-19 across Asia: findings and public health implications for the future.
Front. Public Health
,
8
, 585832, 2020.
68. Gandhi, T.K., Kaplan, G.S., Leape, L., Berwick, D.M., Edgman-Levitan, S., Edmondson, A., Wachter, R., Transforming concepts in patient safety: a progress report.
BMJ Qual. Saf.
, 2, 12, 2018.
69. American Diabetes Association, Standards of medical care in diabetes—2015 abridged for primary care providers.
Clin. Diabetes: A publication of the American Diabetes Association
,
33
, 2, 97, 2015.
70. Hassan, I., Chisty, A., Bui, T., Structural and Social Determinants of Health, in:
Leading an Academic Medical Practice
, pp. 343–355, Springer International Publishing, Cham, 2024.
71. Gan, W.H., Lim, J.W., Koh, D., Preventing intra-hospital infection and transmission of coronavirus disease 2019 in health-care workers.
Saf. Health Work
,
11
, 2, 241–243, 2020.
72. Fang, H., Eggleston, K., Hanson, K., Wu, M., Enhancing financial protection under China’s social health insurance to achieve universal health coverage.
BMJ
,
365
, 91, 2019.
73. Bergwerk, M., Gonen, T., Lustig, Y., Amit, S., Lipsitch, M., Cohen, C., Regev-Yochay, G., Covid-19 breakthrough infections in vaccinated health care workers.
N. Engl. J. Med.
,
385
, 16, 1474–1484, 2021.
74. Tomas, M.E., Kundrapu, S., Thota, P., Sunkesula, V.C., Cadnum, J.L., Mana, T.S.C., Donskey, C.J., Contamination of health care personnel during removal of personal protective equipment.
JAMA Intern. Med.
,
175
, 12, 1904–1910, 2023.
75. Runciman, B., Merry, A., Walton, M.,
Safety and ethics in healthcare: a guide to getting it right
, CRC Press, USA, 2017.
76. Li, Y., Wang, H., Jin, X.R., Li, X., Pender, M., Song, C.P., Wang, Y.G., Experiences and challenges in the health protection of medical teams in the Chinese Ebola treatment center, Liberia: a qualitative study.
Infect. Dis. Poverty
,
7
, 1, 1–12, 2018.
77. Bowman, S., Impact of electronic health record systems on information integrity: quality and safety implications.
Perspect. Health Inf. Manag.
,
10
, Fall, 101, 2023.
78. Watts, N., Adger, W.N., Agnolucci, P., Blackstock, J., Byass, P., Cai, W., Costello, A., Health and climate change: policy responses to protect public health.
Lancet
,
386
, 10006, 1861–1914, 2023.
79. Chartier, Y. (Ed.),
Safe management of wastes from health-care activities
, World Health Organization, USA, 2024.
80. Ogugua, J.O., Anyanwu, E.C., Olorunsogo, T., Maduka, C.P., Ayo-Farai, O., Ethics and strategy in vaccination: A review of public health policies and practices.
Int. J. Sci. Res. Arch.
,
11
, 1, 883–895, 2024.
81. American Diabetes Association, Standards of medical care in diabetes—2014.
Diabetes Care
,
37
, Supplement_1, S14–S80, 2014.
82. Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K.,
The economic potential of generative AI
, 2023.
83. Malik, S., Tyagi, A.K., Mahajan, S., Architecture, Generative Model, and Deep Reinforcement Learning for IoT Applications: Deep Learning Perspective, in:
Artificial Intelligence-based Internet of Things Systems
, pp. 243–265, 2022.
84. Ghimire, P., Kim, K., Acharya, M., Generative ai in the construction industry: Opportunities & challenges.
Buildings
, MDPI, 1, 33, 2023, arXiv preprint arXiv:2310.04427.
85. Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Ziemba, E., Generative artificial intelligence as a new context for management theories: analysis of ChatGPT.
Cent. Eur. Manag. J.
, 2, 66, 2023.
86. Bandi, A., Adapa, P.V.S.R., Kuchi, Y. E. V. P. K., The power of generative ai: A review of requirements, models, input–output formats, evaluation metrics, and challenges.
Future Internet
,
15
, 8, 260, 2023.
87. Zhao, C.W., Yang, J., Li, J., Generation of hospital emergency department layouts based on generative adversarial networks.
J. Build. Eng.
,
43
, 102539, 2021.
88. Bahroun, Z., Anane, C., Ahmed, V., Zacca, A., Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis.
Sustainability
,
15
, 17, 12983, 2023.
89. Soni, V., Impact of Generative AI on Small and Medium Enterprises’ Revenue Growth: The Moderating Role of Human, Technological, and Market Factors.
Rev. Contemp. Bus. Anal.
,
6
, 1, 133–153, 2023.
90. Frey, C.B. and Osborne, M., Generative AI and the Future of Work: A Reappraisal.
Brown J. World Aff.
, 1, 1–12, 2023.
91. Cheng, Z., Lee, D., Tambe, P., Innovae: Generative AI for understanding patents and innovation. Qsketcher: An environment for composing music for film.
Proceedings of the 4th Conference on Creativity & Cognition
, pp. 157–164, 2022, Available at SSRN 3868599.
92. Kshetri, N., Dwivedi, Y.K., Davenport, T.H., Panteli, N., Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda.
Int. J. Inf. Manage.
, 102716, 1, 2023.
93. Mondal, S., Das, S., Vrana, V.G., How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life.
Technologies
,
11
, 2, 44, 2023.
94. Rane, N., Choudhary, S., Rane, J., Integrating Building Information Modelling (BIM) with ChatGPT, Bard, and similar generative artificial intelligence in the architecture, engineering, and construction industry: applications, a novel framework, challenges, and future scope.
Bard, and similar generative artificial intelligence in the architecture, engineering, and construction industry: applications, a novel framework, challenges, and future scope (November 22, 2023)
, 2023.
95. Miao, H., Li, C., Wang, J., A future of smarter digital health empowered by generative pretrained transformer.
J. Med. Internet Res.
,
25
, e49963, 2023.
96. Leone, D., Schiavone, F., Appio, F.P., Chiao, B., How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem.
J. Bus. Res.
,
129
, 849–859, 2021.
97. Maddipoti, A., Pathway Forward for Responsible Generative AI Implementation in Healthcare (Doctoral dissertation), 2023.
98. Ratten, V. and Jones, P., Generative artificial intelligence (ChatGPT): Implications for management educators.
Int. J. Manage. Educ.
,
21
, 3, 100857, 2023.
99. Vyas, B. and Rajendran, R.M., Generative Adversarial Networks for Anomaly Detection in Medical Images.
Int. J. Multi. Innov. Res. Methodol.
,
2
, 4, 52–58, 2023, ISSN: 2960-2068.
100. Rane, N., Roles and challenges of ChatGPT and similar generative artificial intelligence for achieving the sustainable development goals (SDGs).
Socially Responsible Investment eJournal
, 2, 55, 2023, Available at SSRN 4603244.
101. Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Wright, R., “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy.
Int. J. Inf. Manage.
,
71
, 102642, 2023.
