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Generative Artificial Intelligence in Ophthalmology

Edited by Binod Kumar Mishra, Abhishek Kumar, K. Mariyappan, Vibha Tiwari, Pramod Singh Rathore, and Gaur Hari Das
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394358172 | Hardcover |
412 pages
Price: $225 USD

One Line Description
Empower your clinical practice to lead the future of vision science with this guide to mastering generative AI strategies that transform complex imaging data into high-precision, ethical diagnostic solutions.

Audience
Research scholars, computer engineers, ophthamologists and eyecare professionals leveraging generative AI to deliver better patient outcomes and transform the future of vision care.

Description
Generative AI is a game-changer for both artificial intelligence and healthcare, especially in the field of vision science. As Ophthalmologists and other eye care professionals work towards increasing the accuracy of diagnoses and treatment outcomes to improve patient-centered service delivery, generative AI is showing emerging potential as a tool to accomplish these goals. Generative AI, particularly generative adversarial networks, shows potential as a solution for image enhancement, data management, and personalized patient care. This cross-disciplinary work addresses not only the technical challenges in building effective AI systems but also the ethical considerations of using these technologies in the clinical scenario. This two-volume set solves the problem of understanding how advanced AI, specifically generative AI, can be applied effectively in ophthalmology to improve diagnostics, treatment, and accessibility. It addresses the knowledge gap between AI researchers and healthcare professionals by breaking down complex AI concepts into easy-to-understand language, providing practical examples of real-world applications, and guiding readers on the ethical and regulatory challenges of integrating AI into clinical practice.

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Author / Editor Details
Binod Kumar Mishra, PhD is an Associate Professor at Chandigarh University with more than 18 years of experience. He has published ten papers in reputed peer-reviewed journals and international conferences and holds five patents. His research interests include theoretical computer science, natural language processing, and machine learning.

Abhishek Kumar, PhD is an Associate Professor at Chandigarh University with more than 11 years of experience. He has authored seven bookes, edited 30 books, and has more than 100 publications in reputed, peer reviewed national and international journals, books, and conferences. His research interests include artificial intelligence, renewable energy, image processing, computer vision, data mining, and machine learning.

K. Mariyappan, PhD is a Professor in the Department of Computer Science and Engineering at Chandigarh University with an impressive career spanning more than 22 years. He has published 18 papers in reputed journals and conferences and holds two patents. His expertise lies in IoT, wireless sensor networks, and information security.

Vibha Tiwari, PhD is a Professor in the Department of Electronics and Communication Engineering and serves as the Controller of Examinations at Medi-Caps University with more than 22 years of academic experience. She has published ten papers in reputed peer-reviewed journals and international conferences and holds four patents. Her research interests encompass the Internet of Things, embedded systems, image processing, and machine learning.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with more than 11 years of academic teaching experience. He has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, and mining.

Gaur Hari Das, PhD is an Associate Consultant at the B.M. Birla Heart Research Centre. He has more than 120 publications in international journals and conferences of repute. His key area of interest is total arterial coronary artery bypass surgery, where he focuses on advancing surgical techniques and improving patient outcomes.

Table of Contents
Brief Contents of Volume 2
Preface
1. Applying Ethical AI to Synthetic Image Generation in Ophthalmology
Sanvedya Kadam, Piyush Ashokrao Dalke and Neeraja Aswale
1.1 Introduction
1.2 Background on Synthetic Image Generation in Ophthalmology
1.3 Ethical Considerations in AI-Driven Synthetic Image Generation
1.3.1 Data Privacy and Security
1.3.2 Bias and Fairness
1.3.3 Transparency and Explainability
1.3.4 Clinical Reliability and Validation
1.3.5 Informed Consent and Ethical Approval
1.4 Strategies for Ethical AI Implementation in Synthetic Image Generation
1.4.1 Creation of Ethics Policies and Regulations
1.4.2 Increasing Dataset Diversity and Representation
1.4.3 Adding Bias Detection and Mitigation Strategies
1.4.4 Enhancing Cooperation between AI Developers and Clinicians
1.4.5 Working with Explainable and Interpretable AI Models
1.5 Case Studies in Ethical AI Implementation
1.6 Future Directions and Recommendations
1.7 Conclusion
References
2. Generative AI for Low-Resource Vision Care: Creating Synthetic Images
Sanvedya Kadam and Swapna Kamble
2.1 Introduction
2.2 Generative AI and Its Role in Healthcare (Extended)
2.2.1 Overview of Generative AI
2.2.2 The Issues of AI in Medical Imaging
2.2.3 Employing Generative Artificial Intelligence in Medicine
2.2.4 The Role of Generative Cyber-Technologies in Ophthalmic Care
2.2.5 Vision Care Problems in AI Resource-Limited Environments and the Application of AI Solutions
2.3 The Role of Synthetic Images in Vision Care
2.3.1 The Significance of Synthetic Medical Image Data
2.3.2 Applications in Ophthalmology
2.3.2.1 Synthetic Retinal Images for Disease Diagnosis
2.3.2.2 Synthesis of Optical Coherence Tomography (OCT) Images
2.3.2.3 Help in the Development of Fundus Images Needed for Training an AI Model
2.3.2.4 Simulation of Surgical and Treatment Scenarios
2.3.3 Tackling the Issues of Scarcity and Bias in AI Data
2.3.3.1 Dataset Expansion
2.3.3.2 Correcting the Bias in AI Models
2.3.4 Improving AI Model Performance Using Synthetic Data
2.3.5 New Developments in Emulation Techniques for Vision Care
2.4 Generative AI Models for Synthetic Image Generation
2.4.1 Generative AI Technique Overview
2.4.2 Generative Adversarial Networks (GANs) in Vision Care
2.4.2.1 Applications of GANs in Ophthalmology
2.4.2.2 Challenges of Using GANs
2.4.3 Variational Autoencoders (VAEs) for Medical Image Synthesis
2.4.3.1 Applications of VAEs in Vision Care
2.4.3.2 Limitations of VAEs
2.4.4 Diffusion Models for Synthetic Medical Imaging
2.4.4.1 Why Are Diffusion Models Important for Vision Care?
2.4.4.2 Applications in Ophthalmology
2.4.5 Transformer-Based Generative Models
2.4.5.1 Potential Applications
2.4.6 Hybrid Approaches: Combining Multiple Generative Models
2.4.7 Challenges and Ethical Considerations in Synthetic Image Generation
2.4.7.1 Ethical and Regulatory Concerns
2.4.7.2 Technical Limitations
2.4.8 Other Areas of Research in Generative AI for Eye Care
2.5 Challenges and Limitations in Using Generative AI for Low-Resource Vision Care
2.5.1 Quality and Realism of Synthetic Images
2.5.2 Ethical Concerns
2.5.3 Generalization and Transferability
2.6 Future Directions and Opportunities
2.6.1 Using Generative AI in Under-Resourced Areas
2.6.2 AI Collaboration with Human Experts
2.6.3 Multi-Disciplinary Partnerships
2.7 Conclusion
References
3. Cross-Modality Image Synthesis in Vision Care Using Generative AI
Anjali Patil and Sachin Purushottam Untawale
3.1 Introduction
3.2 Generative AI in Medical Imaging
3.2.1 Overview of Generative AI
3.2.2 Application in Imaging Medicine
3.3 Cross-Modality Image Synthesis in Vision Care
3.3.1 Justification for Cross-Modality Synthesis
3.3.2 Main Methods
3.3.2.1 Fundus Image OCT Translation
3.3.2.2 Fluorescein Angiography Synthesis
3.3.2.3 Integration of Images Using AI Technologies’ Multi-Modal Image Fusion
3.4 Challenges in Cross-Modality Image Synthesis
3.4.1 Data Limitations
3.4.2 Model Generalization and Interpretability
3.4.3 Regulatory and Ethical Issues
3.5 Future Directions
3.6 Conclusion
References
4. Synthetic Imaging for Enhanced Vision Diagnostics
B. S. Joshi and Piyush Ashokrao Dalke
4.1 Introduction
4.2 Principles of Synthetic Imaging
4.2.1 Definition and Scope
4.2.2 Core Technologies
4.2.2.1 Machine Learning and AI
4.2.2.2 Computational Optics
4.2.2.3 Augmented and Hybrid Imaging
4.3 Applications in Vision Diagnostics
4.3.1 Retinal Disease Detection
4.3.1.1 Diabetic Retinopathy (DR)
4.3.1.2 Glaucoma
4.3.1.3 Age-Related Macular Degeneration (AMD)
4.3.2 Corneal and Anterior Segment Disorders
4.4 Advantages of Synthetic Imaging
4.4.1 Enhanced Diagnostic Accuracy
4.4.2 Cost Maximization and Broad Accessibility
4.4.3 Real-Time Decision Support
4.5 Challenges and Limitations
4.5.1 Data Quality and Bias
4.5.2 Computational Complexity
4.5.3 Clinical Integration
4.5.4 Ethical and Regulatory Considerations
4.6 Future Directions
4.6.1 Emerging Technologies
4.6.2 Ethical and Regulatory Considerations
4.7 Conclusion
References
5. Generative AI for Low-Resource Settings: Enhancing Ophthalmic Diagnostics
Girish Arun Gadre and Shamla Mantri
5.1 Introduction
5.2 Background on Generative AI
5.3 Challenges in Ophthalmic Diagnostics in Low-Resource Settings
5.4 Applications of Generative AI in Ophthalmic Diagnostics
5.4.1 Image Enhancement and Super-Resolution
5.4.2 Synthetic Data Creation for AI Training Purposes
5.4.3 Automated Disease Classifications
5.4.4 Augmented Telemedicine Solutions
5.4.5 Predictive and Early Disease Detection
5.4.6 Personalized Treatment Recommendations
5.5 Ethical and Implementation Challenges
5.6 Future Directions and Roadmap for Implementation
Conclusion
References
6. Ethical AI in Ophthalmology: Improving Model Accuracy with GANs
Prajakta Patil and Jiwan Dehankar
6.1 Introduction
6.1.1 The Rise of AI in Healthcare
6.1.2 The Contribution of AI in Ophthalmology
6.1.3 Difficulties with Diagnostic Techniques in Ophthalmology
6.1.4 Review of GANs in Healthcare
6.2 Background on Ethical AI
6.2.1 The Meaning of Ethical AI
6.2.2 The Significance of AI Ethics in Healthcare
6.2.3 AI Ethical Considerations
6.2.4 Ethical AI in the Context of Ophthalmology
6.2.5 The Role of Governance and Regulation in Ethical AI
6.3 GANs in Ophthalmology
6.3.1 An Overview of Generative Adversarial Networks (GANs)
6.3.2 Applications of GANs in Ophthalmology
6.3.3 Advancements in Image Quality Using GANs
6.3.4 Improving Model Precision Using GANs
6.3.5 Ethical Issues Associated with the Use of GANs in Ophthalmology
6.3.6 Other Potential Uses of GANs in Eye Care
6.4 Case Studies and Real-World Applications
6.4.1 Case Study 1: Diabetic Retinopathy Diagnosis
6.4.2 Case Study 2: Age-Related Macular Degeneration
6.4.3 Case Study 3: Glaucoma
6.5 Ethical Challenges in the Deployment of AI Models in Ophthalmology
6.5.1 Data Privacy and Security
6.5.2 Algorithmic Transparency
6.5.3 Bias and Fairness in AI Models
6.5.4 Laws and Policies
6.6 Conclusion
References
7. Ethical and Regulatory Considerations in AI-Driven Ophthalmology
Prajakta Patil and Abhay Kashetwar
7.1 Introduction
7.2 Ethical Considerations
7.2.1 Protecting Privacy and Security of Data
7.2.2 Algorithmic Bias and Fairness
7.2.3 Informed Consent and Autonomy
7.2.4 Accountability and Liability
7.3 Regulatory Considerations
7.3.1 Current Regulatory Structures
7.3.2 Challenges in AI Regulation
7.3.3 Standardization and Interoperability
7.3.4 Ethical AI Governance
7.4 Proposed Solutions and Recommendations
7.4.1 Strengthening Data Protection Measures
7.4.2 Addressing Algorithmic Bias
7.4.3 Enhancing Patient Education and Transparency
7.4.4 Forming Distinct Liability Frameworks
7.4.5 Continuous Regulatory Adaptation
Conclusion
References
8. Ethics and Regulations in Generative AI for Vision Diagnostics
Girish Arun Gadre and Fazil Sheikh
8.1 Introduction
8.2 The Role of Generative AI in Vision Diagnostics
8.3 Ethical Concerns in Generative AI for Vision Diagnostics
8.3.1 Bias and Fairness
8.3.2 Privacy and Data Security
8.3.3 Transparency and Explainability
8.3.4 Accountability and Liability
8.4 Regulatory Frameworks Governing AI in Vision Diagnostics
8.4.1 Current Regulations
8.4.2 Difficulties in AI Policies and Guidelines
8.4.3 Proposed Regulatory Strategies
8.4.4 Ethical AI Framework for Vision Diagnostics
Conclusion
References
9. Generative AI in Ophthalmology: Precision in Treatment Prediction
Girish Arun Gadre and Sanjay L. Badjate
9.1 Introduction
9.2 Generative AI in Medical Imaging
9.3 AI-Driven Disease Diagnosis
9.3.1 Enhancements Through Generative AI
9.3.2 Key Diseases Diagnosed with AI
9.4 Precision Treatment Prediction
9.5 Challenges and Ethical Considerations
9.6 Future Prospects
Conclusion
References
10. Generative AI in Ophthalmology: Overcoming GAN Implementation Challenges
Gaurav Paranjpe and Rahul Pethe
10.1 Introduction
10.2 Generative Adversarial Networks (GANs) in Ophthalmology
10.3 Challenges in Implementing GANs in Ophthalmology
10.3.1 Data Limitations and Quality
10.3.2 Model Generalizability
10.3.3 Interpretability and Explainability
10.3.4 Computational Complexity
10.3.5 Ethical and Regulatory Concerns
10.4 Future Recommendations
10.5 Conclusion
References
11. Generative AI for Low-Resource Vision Care: Enabling Patient-Specific Modeling
B. S. Joshi and Sanjay L. Badjate
11.1 Introduction
11.2 Background and Related Work
11.2.1 Generative AI in Medical Imaging
11.2.2 Gaps Associated with Vision Care in Low-Resource Settings
11.3 Generative AI for Patient-Specific Modeling
11.3.1 Data Augmentation for Enhanced Diagnostic Accuracy
11.3.2 Image Reconstruction and Super-Resolution
11.3.3 Personalized Treatment Using Synthetic Patient Data
11.3.4 Anomaly Detection and Automated Segmentation
11.4 Implementation Challenges
11.4.1 The Lack of Data and Training of the Model
11.4.2 Computation and Infrastructure Limitations
11.4.3 Ethical and Regulatory Considerations
11.5 Future Directions
11.6 Conclusion
References
12. Advancing Ophthalmic Diagnostics with Generative AI
V. H. Karambelkar and Kalpana Malpe
12.1 Introduction
12.2 Generative AI: An Overview
12.3 Applications of Generative AI in Ophthalmology
12.3.1 Disease Detection and Classification
12.3.2 Image Improvement and Augmentation
12.3.3 Predictive Analytics and Disease Progression Modeling
12.3.4 Personalized Medicine and Treatment Optimization
12.4 Optical Coherence Tomography (OCT), Fundus Photography, and Fluorescein Angiography
12.4.1 Data Privacy and Security
12.4.2 Bias and Generalization
12.4.3 Validation from a Regulatory and Clinical Perspective
12.4.4 Trust and Ethical Concerns of AI
12.5 Future Directions
12.6 Conclusion
References
13. Leveraging Generative AI for Assistive Technologies in Vision Care
Sonali Patil and K. Gavhale
13.1 Introduction
13.2 Generative AI in Vision Care
13.2.1 Image Enhancement and Super-Resolution
13.2.2 Text-to-Speech and Speech-to-Text
13.2.3 Object Recognition and Scene Understanding
13.2.4 Predictive Analytics and Early Diagnosis
13.3 Challenges and Ethical Considerations
13.3.1 Data Privacy and Security
13.3.2 Bias and Fairness in AI Models
13.3.3 Usability and Accessibility
13.4 Future Directions
13.5 Conclusion
References
14. AI in Low-Resource Settings: Enhancing Ophthalmic Models with Synthetic Data
Gaurav Paranjpe and Salim Chavan
14.1 Introduction
14.2 Challenges in Low-Resource Settings
14.2.1 Scarcity of Verifiable Ophthalmic Data
14.2.2 Ethical and Privacy Concerns
14.2.3 Computational Constraints
14.2.4 Lack of Expert Annotations
14.3 Synthetic Data Generation Techniques
14.3.1 Generative Adversarial Networks (GANs) for Retinal Imaging
14.3.2 Approaches of Data Augmentation
14.3.3 Transfer Learning and Domain Adaptation
14.3.4 Simulation-Based Data Generation
14.4 Enhancing Ophthalmic AI Models with Synthetic Data
14.5 Ethical and Regulatory Considerations
14.5.1 Ensuring Data Authenticity and Reliability
14.5.2 Addressing Bias and Fairness Issues
14.5.3 Compliance with Medical Regulations
14.5.4 Patient Privacy and Security
14.6 Case Studies and Real-World Applications
14.6.1 Detection of Diabetic Retinopathy Using AI Systems
14.6.2 Remote Clinic-Based Glaucoma Screening
14.6.3 Improving Telemedicine Solutions
14.6.4 Ophthalmic Training Programs Incorporating AI
14.7 Challenges and Limitations
14.7.1 Validation and Standardization
14.7.2 Possible Overfitting Issues
14.7.3 Social Concerns on AI Autonomy
14.7.4 Resources and Hardware Requirements
14.7.5 Building Trust in AI as It Relates to Health Care
14.8 Conclusion
References
15. Boosting Ophthalmic Model Accuracy with Generative AI and Synthetic Imaging
Renuka Sarwate and Faisal Hussain
15.1 Introduction
15.2 The Role of AI in Ophthalmology
15.2.1 AI in Disease Detection and Diagnosis
15.2.2 Obstacles Encountered with Artificial Intelligence Model Training
15.3 Generative AI and Synthetic Imaging
15.3.1 Generative Adversarial Networks (GANs)
15.3.2 Applications of GANs in Ophthalmology
15.3.3 Variational Autoencoders (VAEs)
15.3.4 Uses of VAEs in Ophthalmology
15.3.5 Diffusion Models
15.3.6 Advantages of Diffusion Models
15.4 Evaluating Synthetic Imaging Quality
15.5 Case Studies and Experimental Results
15.6 Challenges and Ethical Considerations
15.7 Future Directions
15.8 Conclusion
References
16. Overcoming GAN Challenges to Improve Ophthalmic Model Accuracy
Gaurav Paranjpe and K. Gavhale
16.1 Introduction
16.2 Challenges in GANs for Ophthalmic Applications
16.2.1 Mode Collapse
16.2.2 Training Instability
16.2.3 Data Quality and Augmentation
16.2.4 Evaluation and Validation of Images Created by GANs
16.2.5 Understandability and Confidence in Data Produced by GANs
16.3 Architectural Improvements to Enhance GAN Performance
16.3.1 Conditional GANs (CGANs)
16.3.2 StyleGAN for High-Resolution Image Synthesis
16.3.3 Attention-Dictated GANs
16.3.4 Domain Adaptation Using CycleGAN
16.3.5 Progressive Growing GANs (PGGANs)
16.3.6 Hybrid GAN Architectures
16.4 Real-World Applications and Case Studies
16.4.1 Diabetic Retinopathy Detection
16.4.2 Glaucoma Progression Monitoring
16.4.3 A Simulation of Age-Related Macular Degeneration (AMD)
16.4.4 Applications of Multi-Modal GAN
16.4.5 Augmenting Datasets of Rare Diseases
16.5 Ethical and Regulatory Considerations
16.5.1 Ethical Issues
16.5.2 Issues with Regulations
16.5.3 Potential Solutions
16.6 Future Research Directions
16.7 Conclusion
References
17. Patient-Specific Ophthalmic Modeling with Generative AI
D. B. Shirke and Sanjay Badjate
17.1 Introduction
17.2 Generative AI in Ophthalmology
17.2.1 Generative Adversarial Networks (GANs)
17.2.2 Variational Autoencoders (VAEs)
17.3 Applications of Patient-Specific Ophthalmic Modeling
17.3.1 Personalized Disease Diagnosis
17.3.2 Treatment Planning and Outcome Prediction
17.3.3 Image Enhancement and Restoration
17.3.4 Simulation of Disease Progression
17.4 Challenges in Patient-Specific Ophthalmic Modeling
17.4.1 Data Privacy and Security
17.4.2 Model Interpretability
17.4.3 Imbalanced and Biased Data
17.4.4 Computational and Cost Constraints
17.5 Future Directions and Emerging Trends
17.5.1 XAI in Ophthalmology
17.5.2 Integration with Wearable and Mobile Technologies
17.5.3 Multimodal AI Models
17.5.4 AI-Augmented Decision Support Systems
17.6 Conclusion
References
18. Synthetic Image Generation for Patient-Specific Ophthalmic Models: A Comprehensive Review
Anjali Patil and G. M. Vaidya
18.1 Introduction
18.2 Fundamentals of Synthetic Image Generation
18.2.1 Definition and Importance
18.2.2 Comparison of Traditional Image Synthesis Methods to AI-Driven Methods
18.2.3 Principal Science: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models
18.3 Applications in Ophthalmology
18.3.1 Monitoring Disease Progression and Diagnosis
18.3.2 Simulation and Surgical Planning
18.3.3 Personalization of Treatments
18.3.4 Medical Training and Education
18.3.5 Synthetic Image Application in Medicine
18.4 Techniques for Synthetic Image Generation in Ophthalmology
18.4.1 Generative Adversarial Networks (GANs)
18.4.2 Variational Autoencoders (VAEs)
18.4.3 Diffusion Models
18.4.4 Hybrid Approaches
18.5 Challenges and Limitations
18.5.1 Data Privacy and Ethical Considerations
18.5.2 Biases and Generalizability of the Models
18.5.3 Computational Resources
18.5.4 Validation and Regulatory Endorsement
18.6 Future Directions and Innovations
18.6.1 Integration with Multimodal Imaging
18.6.2 Explainability and Interpretability of AI Models
18.6.2.1 Importance of Explainability in AI-Generated Images
18.6.2.2 Techniques for Improving AI Interpretability
18.6.2.3 Gaps in AI Explainability
18.6.3 Synthetic Image Creation in Real Time
18.6.4 AI-Integrated Services in Ophthalmology Telemedicine
18.7 Conclusion
References
19. Cross-Modality Image Synthesis for Personalized Vision Treatment
V. H. Karambelkar and Himanshu Wagh
19.1 Introduction
19.2 Cross-Modality Image Synthesis Techniques
19.2.1 Generative Adversarial Networks (GANs)
19.2.2 Variational Autoencoders (VAEs)
19.2.3 Diffusion Models
19.2.4 Transformer-Based Approaches
19.3 Applications in Personalized Vision Treatment
19.4 Challenges in Cross-Modality Image Synthesis
19.4.1 Data Availability and Quality
19.4.2 Model Interpretability and Explainability
19.4.3 Ethical and Regulatory Considerations
19.4.4 Computational Complexity and Resource Requirements
19.4.5 Generalizability and Domain Adaptation
19.5 Future Directions
19.6 Conclusion
References

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Generative Artificial Intelligence in Ophthalmology