Generative Artificial Intelligence for Biomedical and Smart Health Informatics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 990

Veröffentlichungsjahr: 2025

Beschreibung

Enables readers to understand the future of medical applications with generative AI and related applications

Generative Artificial Intelligence for Biomedical and Smart Health Informatics delivers a comprehensive overview of the most recent generative AI-driven medical applications based on deep learning and machine learning in which biomedical data is gathered, processed, and analyzed using data augmentation techniques. This book covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data.

The book explores findings obtained by explainable AI techniques, with coverage of various techniques rarely reported in literature. Throughout, feedback and user experiences from physicians and medical staff, as well as use cases, are included to provide important context.

The book discusses topics including privacy and security challenges in AI-enabled health informatics, biosensor-guided AI interventions in personalized medicine, regulatory frameworks and guidelines for AI-based medical devices, education and training for building responsible AI solutions in healthcare, and challenges and opportunities in integrating generative AI with wearable devices.

Topics covered include:

• Treatment of neurological disorders using intelligent techniques and image-guided and tomography interventions for neuromuscular disorders
• Bio-inspired smart healthcare service frameworks with AI, machine learning, and deep learning, integration of IoT devices, and edge computing in industrial and clinical systems
• Traffic management and optimization in distributed environments, patient data management, disease surveillance and prediction, and telemedicine and remote monitoring
• Education-driven, peer-to-peer, and service-oriented architectures and transparency and accountability in medical decision-making

Generative Artificial Intelligence for Biomedical and Smart Health Informatics is an essential reference for computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence and other related technologies in healthcare.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Editors

List of Contributors

Preface

Target Audience

Closing Remarks

Acknowledgments

1 Generative AI in Wearables: Exploring the Impact of GANs, VAEs, and Transformers

1.1 Introduction

1.2 Theoretical Foundations

1.3 Opportunities of Integration

1.4 Research and Development Insights

1.5 Ethical and Regulatory Considerations

1.6 Case Studies and Applications

1.7 Future Directions and Emerging Trends

1.8 Conclusion

References

Note

2 Safeguarding Privacy and Security in AI-Enabled Healthcare Informatics

2.1 Introduction

2.2 Drawbacks and Their Possible Solutions

2.3 Applications

2.4 Devices

2.5 Future Scope

2.6 Conclusion

2.7 Future Scope

References

3 Generating Synthetic Medical Data Using GAI

3.1 Introduction

3.2 Uncloaking the GAI Orchestra: A Compendium of Techniques

3.3 Beyond the Notes: Ethical Considerations and Responsible Use

3.4 Conclusion

References

4 Automation of Drug Design and Development

4.1 Introduction

4.2 High-Throughput Screening (HTS)

4.3 Artificial Intelligence (AI) and Machine Learning (ML)

4.4 Automation in Drug Synthesis and Optimization

4.5 Automation in Clinical Trials

4.6 Challenges and Opportunities

4.7 Conclusion

References

5 Autism Spectrum Disorder Diagnosis: A Comprehensive Review of Machine Learning Approaches

5.1 Introduction

5.2 Machine Learning and Deep Learning Algorithms

5.3 Discussion

5.4 Future Work

5.5 Conclusion

References

6 Temporal Normalization and Brain Image Analysis for Early-Stage Prediction of Attention Deficit Hyperactivity Disorder (ADHD)

6.1 Introduction

6.2 Exploratory Data Analysis

6.3 Methodology

6.4 Results and Discussion

6.5 Conclusion

References

7 Sustainable Agriculture Through Advanced Crop Management: VGG16-Based Tea Leaf Disease Recognition

7.1 Introduction

7.2 Literature Survey

7.3 Proposed Methodology for Tea Leaf Diseases Detection

7.4 Results and Discussion

7.5 Conclusion

References

8 Advancing Colorectal Cancer Diagnosis: Integrating Synthetic Data and Machine Learning for Microbiome Analysis

8.1 Colorectal Cancer (CRC)

8.2 Understanding the Gut Microbiome

8.3 Influence of the Gut Microbiome Dysbiosis on Colorectal Adenomas and CRC

8.4 Differentiating Adenomatous Polyps (AP) from CRC

8.5 Use of Data Augmentation

8.6 Data Evaluation Metrics

8.7 Feature Extraction by Later-Wise Relevance Propagation

8.8 Beta Diversity Analysis

8.9 Machine Learning and SHAP Analysis to Classify AP and CRC Samples

8.10 Results of Classification and SHAP Analysis

8.11 Key Bacterial Taxa Discriminating Between AP and CRC: Insights from Feature Extraction and SHAP Analysis

8.12 Conclusion

References

9 Recent Knowledge in Drug Design and Development: Automation and Advancement

9.1 Introduction

9.2 Automation in Drug Design and Development

9.3 Tools and Database for Drug Design, including Algorithm and Application

9.4 Automation in Drug Design and Its Impact on the Pharmaceutical Sector

9.5 Automation-Assisted Successful Studies in Drug Design

9.6 Advancement and Challenges

9.7 Conclusion

References

Notes

10 Machine Learning and Generative AI Techniques for Sentiment Analysis with Applications

10.1 Introduction

10.2 Literature Review

10.3 Machine Learning Techniques for Sentiment Analysis

10.4 Generative AI Techniques for Sentiment Analysis

10.5 Conclusion

References

11 Use of AI with Optimization Techniques: Case Study, Challenges, and Future Trends

11.1 Introduction

11.2 Overview of Medical Disease Prediction Models

11.3 Importance of Optimization in Enhancing Prediction Accuracy

11.4 Commonly Used Optimization Algorithms in Medical Predictive Modeling

11.5 Integration of ML and Optimization for Disease Prediction

11.6 Challenges and Considerations in Applying Optimization Techniques to Medical Data

11.7 Case Studies: Successful Applications of Optimization in Disease Prediction

11.8 Future Directions and Emerging Trends in Optimizing Medical Prediction Models

11.9 Ethical and Regulatory Implications of Optimized Disease Prediction Systems

11.10 Conclusion: Harnessing Optimization for Advancements in Medical Predictive Analytics

11.11 Future Scope

References

12 Inclusive Role of Internet of (Healthcare) Things in Digital Health: Challenges, Methods, and Future Directions

12.1 Introduction

12.2 The Internet of Medical Things’ (IoMT) Revolution in Healthcare

12.3 The Integration Between Internet of (Healthcare) Things and Digital Health

12.4 Blockchain Applications in the Healthcare Systems

12.5 Healthcare IoT Future Directions: For Digital Health

12.6 Conclusion

References

13 Generating Synthetic Medical Dataset Using Generative AI: A Case Study

13.1 Introduction

13.2 Methodology

13.3 Results

13.4 Conclusion

References

14 A Comprehensive Review of Cardiac Image Analysis for Precise Heart Disease Diagnosis Using Deep Learning Techniques

14.1 Introduction

14.2 Literature Review

14.3 Machine Learning Methods

14.4 Proposed System

14.5 Mathematical Model

14.6 Data Preparation

14.7 Results and Discussion

14.8 Conclusion and Future Work

References

15 Classification Methods of Deep Learning for Detecting Autism Spectrum Disorder in Children (4–12 Years)

15.1 Introduction

15.2 Relevant Work

15.3 Proposed Methodology

15.4 Results

15.5 Conclusion

References

16 Deep Learning Model for Resolution Enhancement of Biomedical Images for Biometrics

16.1 Introduction

16.2 Model

16.3 Experiments and Results

16.4 Conclusion

References

17 Tackling the Complexities of Federated Learning

17.1 Introduction

17.2 Why We Come to Federated Learning

17.3 Related Work

17.4 Challenges in Federated Learning

17.5 Techniques Used in Federated Learning

17.6 Applications

17.7 Result and Analysis

17.8 Conclusion

References

18 Revolutionizing Healthcare: The Impact of AI-Powered Sensors

18.1 Introduction

18.2 Evolution of Healthcare Technology

18.3 Understanding AI-Powered Sensors

18.4 Enhancing Patient Monitoring and Diagnosis

18.5 Improving Treatment Outcomes

18.6 Remote Healthcare and Telemedicine

18.7 Challenges and Ethical Considerations

18.8 Regulatory Landscape

18.9 Future Directions and Opportunities

18.10 Case Studies and Success Stories

References

19 GAI and Deep Learning-Based Medical Sensor Data Relationship Model for Health Informatics

19.1 Introduction

19.2 Related Work

19.3 DSRF Based on Dynamic and Static Relationships Fusion of Multisource Health Sensing Data

19.4 Experiments and Analysis

19.5 Conclusion

References

20 Leveraging Generative Adversarial Networks for Image Augmentation in Deep Learning

20.1 Introduction

20.2 Literature Review

20.3 Material and Method

20.4 Result and Discussion

20.5 Conclusion

References

21 Exploring Trust and Mistrust Dynamics: Generative AI-Curated Narratives in Health Communication Media Content Among Gen X

21.1 Background

21.2 Related Work

21.3 Theoretical Framework

21.4 Research Methodology

21.5 Data Analysis

21.6 Results

21.7 Conclusions and Discussion

References

22 Generative Intelligence-Based Federated Learning Model for Brain Tumor Classification in Smart Health

22.1 Introduction

22.2 Classification Model

22.3 Experiment

22.4 Conclusion

References

23 AI-Based Emotion Detection System in Healthcare for Patient

23.1 Introduction

23.2 Literature Survey

23.3 AI in Healthcare Sector

23.4 Methodology

23.5 Conclusion

References

24 Leveraging Process Mining for Enhanced Efficiency and Precision in Healthcare

24.1 Introduction

24.2 Process Mining

24.3 Main Focus of the Chapter

24.4 Problems

24.5 Solution

24.6 Tools

24.7 Ways Process Mining Solves Healthcare

24.8 One Solution: Robotic Process Automation (RPA)

24.9 Case Study: Process Mining for Optimized COVID-19 ICU Care

24.10 Conclusion

References

25 Transform Drug Discovery and Development With Generative Artificial Intelligence

25.1 Introduction

25.2 Dataset, Molecular Representation, and Benchmark Platforms in Molecular Generation

25.3 Deep Generative Model Architectures

25.4 AI Applications in Drug Discovery and Development

25.5 Challenges and Future Outlooks

Acknowledgments

References

26 Medical Image Analysis and Morphology with Generative Artificial Intelligence for Biomedical and Smart Health Informatics

26.1 Introduction

26.2 Medical Imaging

26.3 Various Types of Modalities

26.4 Medical Imaging Analysis

26.5 Conventional Morphological Image Processing

26.6 Rotational Morphological Processing

References

27 Machine Learning Applications in the Prediction of Polycystic Ovarian Syndrome

27.1 Introduction

27.2 Literature Review

27.3 ML Techniques for Polycystic Ovarian Syndrome

27.4 Artificial Neural Network and Deep Learning

27.5 Challenges

27.6 Conclusion

References

28 Diagnosis and Classification of Skin Cancer Using Generative Artificial Intelligence (Gen AI)

28.1 Introduction

28.2 Factors Affecting Skin Cancer Detection

28.3 Different Types of Skin Cancer

28.4 How Common Is Skin Cancer?

28.5 Dermatological Images and Datasets

28.6 Datasets

28.7 Skin Cancer Classification in Typical CNN Frameworks

28.8 Imbalance in Data and Limitations in Disease in Skin Databases

28.9 ML Techniques for Skin Cancer Diagnosis

28.10 Conclusion

References

29 Secure Decentralized ECG Prediction: Balancing Privacy, Performance, and Heterogeneity

29.1 Introduction

29.2 Parsing ECG Data

29.3 FL for Decentralized ECG Prediction

29.4 Security and Privacy in FL

29.5 Addressing Heterogeneity in ECG Dataset

29.6 Case Study: Advancing Heart Disease Prediction with Asynchronous Federated Deep Learning

29.7 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Health and fitness monitoring case studies with wearables.

Table 1.2 Mental health and well-being case studies with wearables.

Table 1.3 Chronic disease management case studies with wearables.

Table 1.4 Emergency response and elderly care case studies with wearables.

Table 1.5 Future directions and emerging trends.

Chapter 5

Table 5.1 Summary of supervised/unsupervised machine learning algorithms uti...

Table 5.2 Algorithmic efficiency of various machine learning algorithms for ...

Chapter 6

Table 6.1 A comparative analysis of machine learning algorithms on phenotypi...

Chapter 7

Table 7.1 Results of evaluation metric for the leaf disease detection.

Chapter 9

Table 9.1 Useful tools and databases along with their associated features.

Table 9.2 Successful studies employing automation in drug design.

Chapter 10

Table 10.1 Summary of reviewed literature.

Chapter 14

Table 14.1 Accuracy performance.

Table 14.2 Error performance employing different algorithms.

Chapter 15

Table 15.1 Top five countries with highest autism rate in 2023.

Table 15.2 Consequences if autism spectrum disorder in children is not treat...

Table 15.3 Some additional mental disorders that accompany with autism.

Table 15.4 Countries with the least cases of autism in children (2023).

Table 15.5 Setbacks of conventional methods to detect autism spectrum disord...

Table 15.6 The literature survey about detecting autism disorder using advan...

Table 15.7 Description of dataset collected to detect ASD in children.

Table 15.8 Instances of children autism dataset.

Table 15.9 The specificity obtained after checking the performance of the mo...

Table 15.10 The accuracy before handling missing data obtained after checkin...

Table 15.11 The sensitivity obtained after checking the performance of the m...

Table 15.12 The error rate obtained after checking the performance of the mo...

Table 15.13 The accuracy after handling missing data obtained after checking...

Table 15.14 The error rate obtained after checking the performance of the mo...

Chapter 16

Table 16.1 Ablation experiment.

Table 16.2 Quantitative comparison of different super-resolution algorithms....

Chapter 17

Table 17.1 Result of different parameters in HE and DP.

Chapter 19

Table 19.1 Comparison of multicategory disease diagnosis task results under ...

Table 19.2 Comparison of disease classification assessment results.

Chapter 20

Table 20.1 Different GAN-based approaches and their results.

Table 20.2 Pros, cons, and future scope of different GAN-based approaches.

Table 20.3 Model performance on original dataset.

Table 20.4 Model performance on the combined dataset

Chapter 21

Table 21.1 Illustrates the meaning of competence, benevolence, and integrity...

Table 21.2 Cronbach’s alpha value of constructs.

Table 21.3 Demographic characteristics of the respondents.

Table 21.4 Measurement model accuracy assessment.

Table 21.5 Fornell–Larcker criterion: discriminant validity.

Table 21.6 Decision on the proposed hypothesis.

Table 21.7 Group statistics.

Table 21.8 Codes used by participants (frequency).

Chapter 22

Table 22.1 RHAM-MResNet-10 parameter settings.

Table 22.2 Comparison of accuracy of residual network models.

Table 22.3 Comparison of accuracy over benchmark network models.

Table 22.4 Comparison of accuracy over existing attention modules.

Table 22.5 Comparison of accuracy over existing models.

Chapter 23

Table 23.1

T

echnology and application for emotion AI in healthcare.

Chapter 25

Table 25.1 Illustrated cheminformatics databases available for drug discover...

Table 25.2 Commonly used cheminformatics tools and ML packages.

Table 25.3 Some common parameters in use to assess DGMs.

Table 25.4 AI platforms for drug discovery and development.

Chapter 27

Table 27.1 Summary of reviewed literature.

Chapter 28

Table 28.1 Summary of previous reviews on skin cancer classification methods...

Table 28.2 Different methods for solving data imbalance and data limitation....

Table 28.3 Characteristics of different skin-disease datasets.

List of Illustrations

Chapter 1

Figure 1.1 Timeline of wearable devices.

Figure 1.2 AI-driven smart glasses ecosystem.

Figure 1.3 GenAI with wearable device.

Figure 1.4 GAN training loop: generating realistic designs.

Figure 1.5 Encoder–decoder data flow: latent space mapping.

Figure 1.6 Smartwatch sensor data flow: transformer model activity inference...

Figure 1.7 Data processing in wearable devices.

Chapter 2

Figure 2.1 Drawbacks of AI in healthcare.

Figure 2.2 User’s interface of the CORONET.

Figure 2.3 Types of machine learning strategies.

Figure 2.4 Process of deep learning: a subset of ML.

Chapter 3

Figure 3.1 Robotic Automation.

Figure 3.2 Multi model ensemble.

Figure 3.3 Multimodal data generation.

Figure 3.4 Information processing.

Figure 3.5 Data visualization process.

Figure 3.6 Experiment design flow chart.

Figure 3.7 Data encoder.

Figure 3.8 Synthetic data module implementation.

Chapter 6

Figure 6.1 ROI.

Figure 6.2 Histogram analysis of ROI.

Figure 6.3 fMRI image of subject 1 in Mango.

Figure 6.4 Time frames in mango.

Figure 6.5 Statistics table.

Figure 6.6 Histogram analysis.

Figure 6.7 Machine learning pipeline for classification of participants in t...

Figure 6.8 Confusion matrix for (i) KNN (ii) random forest (iii) Random Fore...

Figure 6.9 Result of Linear regression algorithm on phenotypic CSV file.

Figure 6.10 Confusion Matrix with classification accuracy of (i) KNN algorit...

Chapter 7

Figure 7.1 Overview of the proposed work.

Figure 7.2 Images showing various diseases from the dataset.

Figure 7.3 Internal process in the VGG16 model.

Figure 7.4 Accuracy plot from VGG16 model.

Chapter 8

Figure 8.1 Principal coordinate analysis of the original OTU table comprehen...

Figure 8.2 Principal coordinate analysis of the original OTU table comprehen...

Figure 8.3 Principal coordinate analysis of the original OTU table after LRP...

Figure 8.4 AUC regarding the classification of the comprehensive OTU table....

Figure 8.5 Summary plot of the SHAP analysis of the comprehensive OTU table....

Figure 8.6 The AUC of classification is depicted for the stool OTU table in ...

Figure 8.7 Summary plot of the SHAP analysis of the stool OTU table.

Figure 8.8 Summary plot of the SHAP analysis of the biopsy OTU table.

Figure 8.9 Summary plot of the SHAP analysis of the saliva OTU table with 91...

Chapter 9

Figure 9.1 Overview of automation in drug design.

Chapter 10

Figure 10.1 Sentiment analysis approaches.

Figure 10.2 ML/AI approach for sentiment analysis.

Chapter 11

Figure 11.1 Use of AI in disease prediction.

Figure 11.2 Types of optimization techniques present.

Figure 11.3 Flowchart for FPO algorithm.

Figure 11.4 Flowchart of DE algorithm.

Figure 11.5 Flowchart of whale optimization algorithm.

Chapter 13

Figure 13.1 YAML configuration of Tabular-ACTGAN model.

Figure 13.2 YAML configuration of Tabular-Differential-Privacy model.

Figure 13.3 YAML configuration of Tabular-LSTM model.

Figure 13.4 Synthetic medical dataset generation workflow using Gretel.

Figure 13.5 Comparison of training and synthetic data of Tabular-ACTGAN, Tab...

Figure 13.6 Comparison of training correlations, synthetic correlation and c...

Figure 13.7 Comparison of evaluation metrics such as: field correlation stab...

Chapter 14

Figure 14.1 Convolutional Neural Network (CNN) Architecture.

Figure 14.2 Precision versus sensitivity versus specificity.

Figure 14.3 Accuracy of various algorithms.

Figure 14.4 MAE chart.

Figure 14.5 Kappa statistics chart.

Figure 14.6 Confusion matrix for ECG heartbeat.

Figure 14.7 Percentage of correctly classified by category.

Figure 14.8 Distribution of heartbeats classified correctly and incorrectly....

Figure 14.9 The training and validation accuracy and loss of a convolutional...

Chapter 15

Figure 15.1 The highest rate of autism diagnosed population countries (2023)...

Figure 15.2 Children autism rate in the US (2017).

Figure 15.3 Portrays stages of autism in children.

Figure 15.4 Countries with the least autism cases in children (2023).

Figure 15.5 ReLU Activation Graph.

Figure 15.6 The framework of the proposed methodology.

Figure 15.7 Correlation matrix for feature selection.

Figure 15.8 CNN architecture.

Figure 15.9 The specificity in the percentage of the chosen models used to d...

Figure 15.10 The Accuracy of models obtained before handling the missing val...

Figure 15.11 The sensitivity of all the models in percentage.

Figure 15.12 The error rate in percentage of the chosen models before handli...

Figure 15.13 The Accuracy of models obtained after handling the missing valu...

Figure 15.14 The error rate of chosen models after handling the missing data...

Chapter 16

Figure 16.1 SCN-LADN.

Figure 16.2 Sparse-coding nonlocal attention module.

Figure 16.3 Reversible transformation module. (a) Down-sampling reversible t...

Figure 16.4 Multi-scale module.

Figure 16.5 Results of SRR of biometric images, using SCN-LADN.

Figure 16.6 Schematic diagram of feature points focused on by different atte...

Figure 16.7 Results of four times reconstruction of biomedical tissue images...

Figure 16.8 Results of different super-resolution algorithms PSNR and SSIM. ...

Chapter 17

Figure 17.1 Collaborative representation of federated learning.

Figure 17.2 Distortion in true gradient by adding random noise before updati...

Figure 17.3 Laplace distribution.

Figure 17.4 Illustration of the encryption and decryption.

Figure 17.5 Values of precision, recall, accuracy, and RMSE.

Chapter 19

Figure 19.1 Disease relationship heat map.

Figure 19.2 Model framework of proposed model.

Figure 19.3 GRU-based dynamic and static relationships fusion.

Figure 19.4 GRU network structure.

Figure 19.5 Comparison of ablation experiment results.

Figure 19.6 (a) The impact of the number of GRU hidden units on model perfor...

Figure 19.7 Patient 1’s disease diagnosis probability and actual disease sta...

Figure 19.8 Patient 2’s disease diagnosis probability and actual disease sta...

Chapter 20

Figure 20.1 GAN workflow.

Figure 20.2 Image classification workflow.

Chapter 21

Figure 21.1 Conceptual model.

Figure 21.2 Required sample size: G*Power software (3.1.9.7 version).

Figure 21.3 Initial steps of research methodology.

Figure 21.4 Structural model assessment results.

Chapter 22

Figure 22.1 RHAM-MResNet-10 network model.

Figure 22.2 Residual hybrid attention module.

Figure 22.3 Residual channel attention block.

Figure 22.4 Residual spatial attention block.

Figure 22.5 Partial brain CT image [4] / IEEE / CC BY 4.0.

Figure 22.6 Comparisons of classification results of residual network models...

Figure 22.7 Comparison of classification results of backbone network models....

Chapter 23

Figure 23.1 Applications of emotion AI in healthcare.

Figure 23.2 Methodology.

Chapter 24

Figure 24.1 Process Mining Lifecycle: From Data to Actionable Insights [2]....

Figure 24.2 Steps in process mining [4].

Figure 24.3 How process mining is implemented in healthcare? [5].

Figure 24.4 Celonis [9].

Figure 24.5 Disco [10].

Figure 24.6 ProM [11].

Figure 24.7 Basic structure of an event log [14].

Figure 24.8 Process flow in hospitals [19].

Figure 24.9 RPA [21].

Figure 24.10 (a) Filtered directly follows graph related to the first wave o...

Chapter 25

Figure 25.1 The multifaceted applications of AI-based methods in drug discov...

Figure 25.2 Different formats of small molecule representations include Keku...

Figure 25.3 Depiction of RNNs in prediction mode (a) and generation mode (b)...

Figure 25.4 Depiction of CNNs.

Figure 25.5 GNNs operate in prediction mode (a) for learning embeddings from...

Figure 25.6 Depiction of VAEs.

Figure 25.7 Depiction of GANs.

Figure 25.8 Illustrations of NF models.

Figure 25.9 Depiction of Transformers.

Chapter 26

Figure 26.1 Brain CT scan.

Figure 26.2 Abdomen MRI scan.

Figure 26.3 PET image.

Figure 26.4 Ultrasound showing unborn baby in womb.

Figure 26.5 X-rays of different body parts.

Figure 26.6 Colonoscopy.

Figure 26.7 Dermoscopy.

Figure 26.8 Diagram showing the RMP method’s flow.

Figure 26.9 Contrast enhancement techniques are compared on a fictitious tes...

Figure 26.10 Original mammographic images are shown in the left-hand panels....

Figure 26.11 Improved radiography images of the chest using the suggested te...

Chapter 27

Figure 27.1 Common symptoms of PCOS.

Figure 27.2 PCOS diagnosis criteria.

Figure 27.3 Workflow of machine learning model.

Figure 27.4 Machine learning architecture for PCOS diagnosis.

Figure 27.5 Data mining tasks.

Figure 27.6 Machine learning predictive model structure-training and testing...

Figure 27.7 Comparison of classification and regression.

Figure 27.8 An example of decision tree structure.

Figure 27.9 Relation between deep learning, machine learning, and artificial...

Figure 27.10 Illustration of a website model for predicting PCOS risk based ...

Chapter 28

Figure 28.1 Melanoma skin cancer, types, stages, signs, symptoms, and treatm...

Figure 28.2 Skin cancer detection and classification.

Figure 28.3 Examples of three types of dermatological images of BCC showing ...

Figure 28.4 Graph 1 different skin-disease datasets and number of images in ...

Chapter 29

Figure 29.1 Cardiolund ECG parser: It is a medical software for automated rh...

Figure 29.2 (a) A sinus rhythm condition illustration. Variation in heart ra...

Figure 29.3 This simplified network illustrates FL for ECG analysis. Partici...

Figure 29.4 An overall workflow for vertical FL. The classic workflow includ...

Figure 29.5 Architecture for a horizontal federated learning system [23].

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Editors

List of Contributors

Preface

Acknowledgments

Begin Reading

Index

End User License Agreement

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief

Moeness Amin

Jón Atli Benediktsson

Adam Drobot

James Duncan

Ekram Hossain

Brian Johnson

Hai Li

James Lyke

Joydeep Mitra

Desineni Subbaram Naidu

Tony Q. S. Quek

Behzad Razavi

Thomas Robertazzi

Diomidis Spinellis

Generative Artificial Intelligence for Biomedical and Smart Health Informatics

Edited by

Aditya Khamparia

Babasaheb Bhimrao Ambedkar University

Amethi, India

Deepak Gupta

Maharaja Agrasen Institute of Technology

Delhi, India

Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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To the inventors of artificial intelligence whose visionary ideas persistently expand the limits of human capabilities and to the biomedical scientists who relentlessly pursue resolutions to the most intricate medical problems.

This book is specifically focused on the convergence of these realms—where technology intersects with empathy, and innovative practices evolve into the process of healing. May the advancements in generative AI evoke optimism, exploration, and a more promising future for worldwide patient care.

About the Editors

Aditya Khamparia is an eminent academician and plays versatile roles and responsibilities toward lectures, research, publications, consultancy, community service, and PhD supervision. With more than 13 years of rich expertise in teaching and two years in industry, he focuses on individual-centric and practical learning. Currently, he is an assistant professor in the Department of Computer Science at Babasaheb Bhimrao Ambedkar University, Lucknow, India. His research areas include machine learning, soft computing, educational technologies, IoT, semantic web, and ontologies. He has published more than 100 scientific research publications in reputed international and national journals and conferences, indexed in various international databases. He has been invited to serve as a Faculty Resource Person, Session Chair, Reviewer or Technical Program Committee (TPC) member for different faculty development programs (FDPs), conferences, and journals. He also serves as a reviewer and member of various renowned national and international conferences and journals.

Deepak Gupta is an eminent academician and plays versatile roles and responsibilities juggling between lectures, research, publications, consultancy, community service, and PhD and post doctorate supervision. He is currently working at Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India. He has served as Editor-in-Chief, Guest Editor, Associate Editor in SCI, and various other reputed journals, including those published by IEEE, Elsevier, Springer, Wiley, and MDPI. He has completed his PhD from Dr. APJ Abdul Kalam Technical University, India, in 2017. He has authored/edited 70 books published by National/International publishers such as IEEE Press, Elsevier, Springer, Wiley, CRC, and DeGruyter. He has published 330 scientific research publications and has been featured in the list of top 2% scientist/researcher database in the world in 2019, 2020, 2022, and 2023. He has received a grant of Rs 1.31 crore from the Department of Science and Technology against the Indo-Russian Joint call.

List of Contributors

Mohammed Abdalla

Faculty of Computers and Artificial Intelligence

Beni-Suef University

Cairo

Egypt

Diksha Aggarwal

CSE, SOET

The NorthCap University

Gurugram

India

Pooja Agarwal

Computer Science

PES

Bangalore

Karnataka

Vanshika Singh Andotra

Department of Information

Technology and Computer Science

Manipal University

Jaipur

India

Komal Arora

School of Computer Science

Lovely Professional University

Phagwara, Punjab

India

Anagha Balakrishnan

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Aaryan Barde

Department of CSE-AIML

LNCT Group of Collage

Bhopal, MP

India

Snehlata Barde

Department of CSECS

PIET Parul University

Vadodara Gujrat

India

Kanaka Durga Prasad Bhamidipaty

Department of Radiology

NRIIMS

Visakhapatnam

India

Veenadhari Bhamidipaty

Department of Computer Science and Engineering

Gandhi Institute of Technology and Management

Visakhapatnam, Andhra Pradesh

India

Durgananda Lahari Bhamidipaty

Department of Biotechnology

Manipal Institute of Technology

Manipal, Karnataka

India

Amit Bhagat

Maulana Azad National Institute of Technology (MANIT)

Bhopal

Suman Bhatia

Department of Artificial Intelligence and Machine Learning

Dr. Akhilesh Das Gupta Institute of Professional Studies (affiliated to Guru Gobind Singh Indraprastha University New Delhi)

New Delhi

Rajesh Botchu

Department of Radiology

NRIIMS

Visakhapatnam

India

and

AHERF

Hyderabad

India

and

Department of Musculoskeletal Radiology

Royal Orthopedic Hospital

Birmingham

UK

Bhuvan Botchu

Solihull School

Solihull

UK

Haewon Byeon

Department of AI and Software

Inje University

Gimhae

Republic of Korea

Poonam Chaudhary

CSE, SOET

The NorthCap University

Gurugram

India

Riddhi Chawla

School of Dentistry

Central Asian University

Tashkent

Uzbekistan

Ganeev Kaur Chhabra

Department of Computer Science & Engineering

BharatiVidyapeeth College of Engineeering

New Delhi

India

Tabsum Chhetri

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Om Dabral

Manipal University

Jaipur

Dharmendra Dangi

Indian Institute of Information Technology (IIITB)

Bhopal

Virrat Devaser

School of Computer Science

Lovely Professional University

Phagwara, Punjab

India

Diwakar Diwakar

BBA University

Lucknow

India

Dheeraj Kumar Dixit

Madhav Institute of Science and Technology (MITS)

Gwalior

Tanay Falor

IIIT

Allahabad

John J. Georrge

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Toshika Goswami

Department of Computer Science & Engineering

BharatiVidyapeeth College of Engineeering

New Delhi

India

Indira Guntoory

Department of Obstetrics & Gynaecology

GIMSR

Visakhapatnam

India

Charu Gupta

Department of Computer Science

Bhagwan Parshuram Institute of Technology

Delhi

India

Anuj Gupta

Department of Electronics and Communication

Chandigarh University

Mohali

India

Rishik Gupta

Department of Information Technology and Computer Science

Manipal University

Jaipur

India

Kusum Gurung

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Ernesto Iadanza

Department of Medical Biotechnologies

University of Siena

Italy

Karthikeyan P. Iyengar

Department of Orthopedics, Southport and Ormskirk Hospital Southport

Mersey and West Lancashire Hospitals NHS Trust

UK

and

AHERF

Hyderabad

India

and

Edge Hill University

Ormskirk

UK

Ati Jain

Institute of Advance Computing

SAGE University

Indore

India

Amiyavardhan Jain

Consultant, Periodontology and Implantology

Noble Dental Care

Indore

India

Akshay Kanwar

Department of Electronics and Communication Engineering

Jawaharlal Nehru Government Engineering college

University Hamirpur

Sundernagar, 175018 Himachal Pradesh

India

Ismail Keshta

Computer Science and Information Systems Department

College of Applied Sciences

AlMaarefa University

Riyadh

Saudi Arabia

Aditya Khamparia

Department of Computer Science

Babasaheb Bhimrao Ambedkar

University (A Central University)

Lucknow

India

Akanksha Kochhar

Department of Computer Science & Engineering

BharatiVidyapeeth College of Engineeering

New Delhi

India

Pramod Kumar

Ganga Institute of Technology and Management

Maharshi Dayanand University

Rohtak, Haryana

India

Vikas Kumar

ERP Department

ERP Functional Riviera Home Furnishing

Panipat

India

Bagesh Kumar

Department of Information Technology and Computer Science

Manipal University

Jaipur

India

Sohan Kumar

Department of Information Technology and Computer Science

Manipal University

Jaipur

India

Ravi Kumar

Department of Computer Science Engineering

Lovely Professional University

Phagwara, 144411 Punjab

India

and

Department of Computer Science Engineering (AIML)

Jawaharlal Nehru Government Engineering college

University Hamirpur

Sundernagar, 175018 Himachal Pradesh

India

Antonio Lavecchia

“Drug Discovery” Laboratory

Department of Pharmacy

University of Naples Federico II

Naples

Italy

Kari Lippert

Department of Systems Engineering

University of South Alabama

Mobile, AL

USA

Niladri Maiti

School of Dentistry

Central Asian University

Tashkent

Uzbekistan

Arish Mallick

Queens University Belfast

UK

Brijendra Pratap Mishra

Department of Biochemistry

Autonomous State Medical College Bahraich

Atal Bihari Vajpayee Medical University

Lucknow, Uttar Pradesh

India

Saurav K. Mishra

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Ayushi Mittal

Department of Computer Science

Indira Gandhi Delhi Technical University for Women

New Delhi

India

Ardra Nair

School of Computer Science

Lovely Professional University

Phagwara, Punjab

India

Aryan Nakhale

Department of Mechatronics

Chandigarh University

Mohali

India

H. Naresh Kumar

School of Arts Sciences Humanities & Education

SASTRA Deemed University

Thanjavur

India

Babita Pandey

Department of Computer Science

Baba Saheb Bhimrao Ambedkar (Central University)

Lucknow

India

Devendra Kumar Pandey

School of Biotechnology

Lovely Professional University

Phagwara, Punjab

India

Sagar Dhanraj Pande

School of Engineering and Technology

Pimpri Chinchwad University (PCU)

Pune, Maharashtra

India

Parul Parul

Department of Computer Science

Indira Gandhi Delhi Technical University for Women

New Delhi

India

Shreyansh Patel

Department of Artificial Intelligence

Sage University

Indore, MP

India

S. Praveena

School of Arts Sciences Humanities & Education

SASTRA Deemed University

Thanjavur

India

Deepti Prasad

Final year Engineering Student in the Department of Artificial Intelligence and Machine Learning

Dr Akhilesh Das Gupta Institute of Professional Studies (affiliated to Guru Gobind Singh Indraprastha University New Delhi)

New Delhi

Aadam Quraishi

M. D. Research

Intervention Treatment Institute

Houston, TX

USA

C.S. Raghuvanshi

Department of Computer Science & Engineering, FET

Rama University

Kanpur

India

Bhallamudi RaviKrishna

Department of Artificial Intelligence and Data Science

Vignan Institute of Technology & Science

Hyderabad

India

Deepa Raj

BBA University

Lucknow

India

Akash Raj

Department of Information Technology and Computer Science

Manipal University

Jaipur

India

Nikki Rani

CSE, SOET

The NorthCap University

Gurugram

India

Partha Pratim Ray

Department of Computer Applications

Sikkim University

Gangtok

India

Yashashwini Reddy

Stanley College of Engineering and Technology for Women

Osmania University

Hyderabad, Telangana

India

Niveditha N. Reddy

Computer Science

PES

Bangalore

Karnataka

Madireddy Vijay Reddy

Department of Artificial Intelligence and Data Science

Vignan Institute of Technology & Science

Hyderabad

India

Chinthala Kishor Kumar Reddy

Stanley College of Engineering and Technology for Women

Osmania University

Hyderabad, Telangana

India

and

Faculty of Engineering and Technology

Botho University

Gaborone

Botswana

Sahithi Reddy

Master of Information Technology and Master of Information Technology Management

The University of Sydney

New South Wales, Sydney

Australia

Alessio Rotelli

Department of Medical Biotechnologies

University of Siena

Italy

Sneha Roy

Department of Bioinformatics

University of North Bengal

Darjeeling, West Bengal

India

Maher Ali Rusho

Department of Lockheed Martin Engineering Management

University of Colorado

Boulder, CO

USA

Srishti Sharma

CSE, SOET

The NorthCap University

Gurugram

India

Seema Shukla

School of Modern Media

UPES

Dehradun

India

Moolchand Sharma

Department of Computer Science & Engineering

Maharaja Agrasen Institute of Technology

New Delhi

India

Parth Sharma

Manipal University

Jaipur

Riya Sharma

School of Computer Science and Engineering

Lovely Professional University

Phagwara

India

Kirti Shukla

SCSE

IILM University Greater Noida

Noida

India

Prakhar Shukla

Department of Information Technology

IIIT Allahabad

Allahabad

India

Balraj Singh

School of Computer Science and Engineering

Lovely Professional University

Phagwara

India

Sudhanshu Singh

Seth Anandram Jaipuria School

Kanpur

India

Suruchi Singh

Department of Computer Science & Engineering; UIET

Chhatrapati Shahu Ji Maharaj University

Kanpur

India

Neelesh Singh

Department of Artificial Intelligence

Sage University

Indore, MP

India

Amritpal Singh

Department of Computer Science Engineering

Lovely Professional University

Phagwara, 144411 Punjab

India

R. Sivaraman

School of Computing

SASTRA Deemed University

Thanjavur

India

Mukesh Soni

Dr. D. Y. Patil Vidyapeeth, Pune

Dr. D. Y. Patil School of Science & Technology

D. Y. Patil University

Tathawade, Pune

India

Devendra K. Tayal

Department of Computer Science

Indira Gandhi Delhi Technical University for Women

New Delhi

India

Raj Thakur

Department of Artificial Intelligence

Sage University

Indore, MP

India

Abhinav Upadhyay

Manipal University

Jaipur

Yash Vikram Singh Rathore

Department of Information Technology and Computer Science

Manipal University

Jaipur

India

Preface

This book focuses on recent advances, roles, and benefits of generative artificial intelligence (AI) in biomedical and smart health informatics. By leveraging deep learning techniques like neural networks, generative AI systems are capable of creating complex data outputs—ranging from synthetic medical images to predictive models of diseases. This technology can assist in diagnosing medical conditions, generating new hypotheses for drug discovery, and personalizing treatment plans. This book aims to describe the different techniques of generative intelligence for health informatics from a practical point of view, solving common life problems. This book also brings a valuable point of view to engineers and businessmen that work in companies, trying to solve practical, economic, and technical problems in the field of their company activities or expertise. The pure practical approach helps to transmit the idea and the aim of the author to communicate the way to approach and to cope with problems that would be intractable in any other way. The integration of generative AI with smart health devices, including wearables, further pushes the boundaries of personalized healthcare. By continually learning from real-time data streams, generative models can predict health issues before they manifest and suggest interventions, leading to a proactive approach to medical care. Despite its promise, challenges remain in ensuring data privacy, model transparency, and overcoming regulatory hurdles, but the ongoing advancements suggest a bright future for AI-driven healthcare solutions.

This paradigm shift not only accelerates biomedical research but also democratizes healthcare by making advanced diagnostics and treatments accessible to a broader population. As generative AI continues to evolve, its role in smart health and biomedical informatics will become even more pronounced, offering transformative benefits across the entire healthcare ecosystem.

The chapters within highlight practical applications across various domains, including genomics, medical imaging, and clinical decision support systems, all driven by AI’s generative capabilities. They also delve into ethical considerations, data privacy, and regulatory concerns, emphasizing the need for responsible and transparent AI integration in healthcare systems.

Target Audience

Target audience of the book comprises professionals and practitioners in the field of intelligent systems, generative machines, deep learning–driven systems, wearable cloud-enabled applications, and ubiquitous computing science paradigms that may be benefited directly from others’ experiences. Graduate and master’s students working on final projects or particular courses related to generative intelligent systems or medical domains can benefit from this book, making the book interesting for engineering and medical university teaching purposes. The research community of intelligent system, data analytics, engineering sciences, computer vision and biomedical applications, consisting of many conferences, workshops, journals and other books, will take this as a reference book.

Closing Remarks

In conclusion, we sum up here with a few lines that this book is a small step toward the enhancement of academic research via motivating the research community and research organizations to think about the impact of generative and federated learning frameworks, networking principles, and their applications in augmenting academic research. This book provides insights into various aspects of academic computing research and the need for knowledge sharing and the prediction of relationships through several links and their usages. It enables the audience to have information about how deep generative AI can be used in different problem scopes of medicine. It will inform the audience about both positive and negative findings obtained by explainable AI techniques. It also includes the use of newly developed explainable AI techniques reported rarely for now in the literature. By excluding research works with basic used datasets and including or focusing majorly on augmented or synthesized data, the book provides a better understanding of the state of generative AI in real-case experiences internationally. Also, by including feedback and user experiences from physicians and medical staff for applied deep-learning-based solutions, the book focuses on popular medical application types of deep AI reported in the associated literature widely.

We hope that research scholars, educationalists, and students alike will find this book significant and continue to use it to expand their perspectives on the field of generative intelligent computing and its future challenges.

Aditya KhampariaBabasaheb Bhimrao Ambedkar University, IndiaDeepak GuptaMaharaja Agrasen Institute of Technology, India

Acknowledgments

We precise our gratitude to the many people; those who contributed, supported, and guided us through this book by different means. This book would not have been possible without their guidance and help.

First and foremost, we express heartfelt gratitude to our Guru for spiritual empathy and incessant blessings, and to all teachers and friends for their continued guidelines and inspiration throughout the period of our studies and careers.

We thank IEEE-Wiley, the publisher who gave us an opportunity to publish with them. We express our appreciation to all contributors including the accepted chapters’ authors, and many other contributors who submitted their chapters that cannot be included in the book. Special thanks to Sandra Garyson, Cowan Becky, Kavipriya Ramchandran, and Vijayalakshmi Saminathan from IEEE-Wiley team for their kind support and great efforts in bringing the book to completion. The encouragement of the Editorial Advisory Board (EAB) cannot be exaggerated. These are renowned experts who took time off their busy schedules to review chapters, provide constructive feedback, and improve the overall quality of chapters.

We thank our dear friends and colleagues for their continuous support and countless efforts throughout the process of publication of this book.

We express our personal and special thanks to our family members for their love, tremendous support, and inspiration throughout our careers which they gave us in all these years.

Last but not least, we request forgiveness from all those who have been with us over the course of the years and whose names we have failed to mention.

Dr. Aditya KhampariaBabasaheb Bhimrao Ambedkar University, IndiaDr. Deepak GuptaMaharaja Agrasen Institute of Technology, India

1Generative AI in Wearables: Exploring the Impact of GANs, VAEs, and Transformers*

Diwakar Diwakar and Deepa Raj

BBA University, Lucknow, India

1.1 Introduction

The integration of advanced generative artificial intelligence (GenAI) models—Generative Adversarial Networks (GANs), Transformers, and Variational Autoencoders (VAEs)—with wearable technology marks a revolutionary leap at the crossroads of personal computing and healthcare. This fusion is not merely evolutionary, but it represents a transformative shift toward crafting systems that are more personalized, adaptive, and intelligent, poised to redefine our daily lives, health management, and interaction with technology. As we navigate this transformative era, it is crucial to delve into the unique capabilities of GANs, Transformers, and VAEs, which stand at the forefront of AI research and application. These models excel in generating new content, ideas, or data patterns that closely mimic human creativity and understanding. From enhancing image quality and creating realistic simulations for health training to offering real-time language translation and personalized health insights, these AI models are pushing the boundaries of what machines can achieve. On the flip side of this integration is the rapidly evolving domain of wearable technology. In the last decade, wearable devices, including smartwatches, fitness trackers, health monitors, and smart glasses, have seen exponential growth in both adoption and capabilities. Equipped with an array of sensors, these devices offer a seamless interface between the digital and physical worlds, collecting and analyzing data in real-time to provide actionable insights directly to the user. The synergy between sophisticated AI models like GANs, Transformers, and VAEs and wearable technology is set to unlock unparalleled opportunities. Envision wearable devices that not only monitor health metrics but also anticipate health issues before they arise, offering personalized advice and interventions tailored to the user’s unique health profile and lifestyle.

However, this promising frontier is not without its challenges. Integrating complex AI models into wearable devices raises significant ethical considerations, particularly concerning AI-generated content and decisions related to health and personal data. Privacy and security are paramount, given the highly personal and sensitive nature of the data collected. Moreover, the technical hurdles of embedding these sophisticated AI models into compact, efficient, and user-friendly devices are substantial. Overcoming these challenges necessitates a multidisciplinary approach, blending expertise in AI, cybersecurity, ethics, and wearable technology design. Refer Figure 1.1 for the timeline of wearable devices.

As we stand on the cusp of this exciting integration, the journey ahead is fraught with obstacles. Yet, the potential benefits for personal health, well-being, and the overall human experience are vast. The integration of GANs, Transformers, and VAEs with wearable technology represents a bold stride toward a future where technology profoundly understands and enhances the human condition. It beckons us to reimagine the limits of personal computing and healthcare, promising a future where our devices transcend their role as mere tools to become partners in fostering a healthier, more personalized, and empowered existence.

1.1.1 Overview of GenAI and Wearable Technology

GenAI encompasses advanced subsets of AI that are capable of producing new content, ideas, or data patterns by learning from existing datasets. This capability is not merely about replication but involves a deep understanding and innovation that mimics human creativity and intelligence. Key models within this domain include.

1.1.1.1 Generative Adversarial Networks

GANs are a class of machine learning frameworks where two neural networks, a generator and a discriminator, are trained simultaneously. The generator creates data resembling the training set, while the discriminator evaluates its authenticity. In wearable technologies, GANs are instrumental in generating synthetic biological data, such as heart rates or blood glucose levels, enhancing privacy by avoiding the use of real user data and improving the robustness of health monitoring algorithms through extensive training datasets (1). This application is crucial for developing predictive models that can accurately forecast health issues or personalize health interventions without compromising individual privacy.

Figure 1.1 Timeline of wearable devices.

1.1.1.2 Variational Autoencoders

VAEs are a type of generative model that learns the probability distribution of training data, allowing it to generate new data points with similar characteristics. In wearables, VAEs can be used for anomaly detection, identifying unusual patterns in physiological data that may indicate emerging health issues (2). In addition, VAEs support the customization of fitness or wellness plans by generating user-specific recommendations based on their unique physiological data patterns.

1.1.1.3 Transformer

Transformer represents a breakthrough in handling sequential data, such as text or time-series health metrics, through self-attention mechanisms. This architecture enables the model to weigh the importance of different parts of the input data, making it highly effective for analyzing and predicting trends in physiological data collected by wearables (3). For instance, transformers can process sequences of heart rate data to identify patterns indicative of health conditions or predict future health states, facilitating timely interventions.

1.1.1.4 Wearable Technology

integrates these AI advancements into compact, user-centric devices designed for continuous health and activity monitoring. Wearable devices leverage a suite of sensors to collect a wide array of data points:

Biometric Sensors

: Measure physiological metrics, such as heart rate (HR), through photoplethysmography (PPG), a noninvasive optical technique that detects blood volume changes.

Motion Sensors

: Including accelerometers and gyroscopes, quantify physical activity and movement patterns by measuring acceleration () and angular velocity (), respectively.

Environmental Sensors

: Assess external factors like temperature and UV exposure, providing context to health data and enhancing the device’s utility.

These sensors generate a continuous data stream (), represented as

(1.1)

where each is a data point collected at time . The integration of GenAI models with wearable technology enables the transformation of this raw data into actionable insights () through a process of data analysis (), modeled as

(1.2)

where represents the parameters of the AI model. This integration promises to revolutionize personal computing and healthcare by offering unprecedented personalization and adaptability, transforming wearable devices from passive data collectors to proactive health and lifestyle coaches.

1.1.2 Significance of Integration: The Future of Personal Computing and Healthcare

The integration of GenAI with wearable technology is significant for several reasons, marking a shift toward more personalized, adaptive, and intelligent systems that promise to reshape personal computing and healthcare.

1.1.2.1 Personalized User Experiences

By analyzing data collected from wearable devices, GenAI can create highly personalized experiences for users. For example, a fitness tracker could generate custom workout plans that adapt to the user’s progress, preferences, and current physical condition or a smartwatch could generate reminders and motivational messages tailored to the user’s habits and goals.

1.1.2.2 Advanced Health Monitoring and Predictive Analytics

GenAI can identify patterns and anomalies in health data that may not be apparent to human observers. This capability allows for early detection of potential health issues, predictive analytics for disease progression, and personalized health advice. For instance, a wearable device could predict the onset of a health condition based on subtle changes in the user’s physiological data, enabling early intervention.

1.1.2.3 Innovative Applications and Services

The combination of GenAI and wearable technology opens up new possibilities for innovative applications and services. For example, wearable devices could generate real-time environmental alerts or navigation aids for the visually impaired users create immersive augmented reality (AR) experiences based on the user’s surroundings, or offer real-time language translation services.

1.1.2.4 Empowering Healthcare Professionals

In a clinical setting, wearable devices integrated with GenAI can provide healthcare professionals with deeper insights into their patients’ conditions, enabling more informed decision-making and personalized care plans. This integration can also facilitate remote monitoring and telehealth services, expanding access to healthcare and reducing the need for in-person visits. Figure 1.2 showcases the potential of GenAI in wearable technology to create immersive, personalized, and context-aware experiences, transforming how users interact with their environment. This diagram showcases a smart glasses ecosystem where a GenAI Module leverages data from a User Preferences Database to provide personalized recommendations. These recommendations are enhanced through environmental analysis and object identification, which analyze surroundings and identify common objects and signs, respectively. The smart glasses then display this tailored information as an information overlay comprising text and symbols to the user.

Figure 1.2 AI-driven smart glasses ecosystem.

1.2 Theoretical Foundations

1.2.1 GenAI: Concepts and Mechanisms

The term “generative artificial intelligence” (GenAI) describes a subset of AI that focuses on producing new information, ideas, or outcomes that are plausible and realistic based on underlying patterns in the input data but do not explicitly exist in the data. It stands out in particular because it can produce data in addition to just analyzing or categorizing it. The fundamental idea behind GenAI is built on machine learning models that produce new, similar instances of data by first learning the distributions of data in a specific domain (such as text, music, or photos).

Figure 1.3 flowchart displayed the integration of GenAI with a smartwatch for personalized health monitoring and advice generation. It begins with the smartwatch collecting health data, which is preprocessed for consistency and clarity. This data is then analyzed by a GenAI model, deployed on a cloud server or edge device, to generate customized health advice. Finally, this advice is displayed on the smartwatch, providing users with actionable insights into their health. This system exemplifies the use of advanced AI in wearable technology to offer real-time, personalized health recommendations, enhancing user health and wellness through data-driven insights.

1.2.1.1 Generative Adversarial Networks

GANs are a fascinating and powerful class of AI algorithms used to generate new data that resembles some given real data. GANs are used in various applications, including image generation, video generation, and voice generation. GANs consist of two major parts:

The Generator, GANs aims to produce data so realistic that it cannot be distinguished from actual data, by learning to transform a latent space of random noise into data that mimics real-world