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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  |  
469 pages
Price: $225 USD
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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.

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Table of Contents
Brief Contents of Volume 1
Preface
20. Personalized Medicine in Vision Care through AI-Driven Image Synthesis
Renuka Sarwate and Rasika Ranjit Chafle
20.1 Introduction
20.2 AI-Driven Image Synthesis: Technological Foundations
20.2.1 Generative Adversarial Networks (GANs)
20.2.2 Variational Autoencoders (VAEs)
20.2.3 Diffusion Models
20.3 Applications of AI-Driven Image Synthesis in Vision Care
20.3.1 Improved Disease Detection
20.3.2 Personalized Treatment Planning
20.3.3 Medical Education and Training
20.3.4 Overcoming Data Limitations
20.4 Benefits of AI-Driven Image Synthesis in Personalized Vision Care
20.5 Ethical Considerations and Challenges
20.6 Future Directions and Innovations
20.7 Conclusion
References
21. Predicting Treatment Outcomes in Ophthalmology with Generative AI
Anjali Patil and Sachin Purushottam Untawale
21.1 Introduction
21.2 Background: AI in Ophthalmology
21.3 Generative AI Techniques
21.4 Applications of Generative AI in Predicting Treatment Outcomes
21.5 Advantages of Generative AI in Ophthalmology
21.6 Challenges and Limitations
21.7 Conclusion
References
22. Predicting Vision Care Outcomes with Personalized Generative AI
D.B. Shirke and P. Bainalwar
22.1 Introduction
22.2 Background and Related Work
22.2.1 AI in Healthcare
22.2.2 AI in Vision Care
22.2.3 Generative AI in Predictive Analytics
22.3 Methodologies
22.3.1 Data Collection and Preprocessing
22.3.2 Generative AI Models in Vision Care
22.3.3 Model Training and Validation
22.4 Applications of Personalized Generative AI in Vision Care
22.5 Challenges and Ethical Considerations
22.6 Conclusion
References
23. Shaping Ophthalmic Imaging: The Challenges of Implementing GANs
Renuka Sarwate and Chandrayani Rokde
23.1 Introduction
23.2 The Role of GANs in Ophthalmic Imaging
23.3 Key Challenges in Implementing GANs in Ophthalmic Imaging
23.3.1 Availability and Quality of Data
23.3.2 Model Generalization and Robustness
23.3.3 Interpretability and Explainability
23.3.4 Ethical and Legal Considerations
23.3.5 Clinical Validation and Adoption
23.4 Future Directions
23.5 Conclusion
References
24. Challenges in Using GANs for Synthetic Imaging in Vision Diagnostics
Sanvedya Kadam and Pankaj Thote
24.1 Introduction
24.2 Background on GANs in Vision Diagnostics
24.2.1 Overview of GANs
24.2.2 Vision Diagnostics Applications
24.3 Challenges in Using GANs for Synthetic Imaging in Vision Diagnostics
24.3.1 The Availability and Quality of Data
24.3.2 Model Interpretability and Reliability
24.3.3 Clinical Validation and Regulatory Challenges
24.3.4 Ethical and Bias Concerns
24.3.5 Computational and Infrastructure Constraints
24.4 Current Solutions and Mitigation Strategies
24.5 Future Directions
24.6 Conclusion
References
25. Generative AI for Assistive Vision Technologies: A New Era in Care
Sonali Patil and Mrudula Nimbarte
25.1 Introduction
25.2 Generative AI: An Overview
25.3 Applications of Generative AI in Assistive Vision Technologies
25.3.1 AI-Generated Image Descriptions
25.3.2 Real-Time Object Recognition and Navigation Assistance
25.3.3 Scene Reconstruction and Enhancement
25.3.4 Improvements in Text-to-Speech and Speech-to-Text Systems
25.3.5 AI-Powered Braille and Tactile Graphics Generation
25.4 Challenges and Limitations
25.4.1 Data Bias and Ethical Issues
25.4.2 Computation and Hardware Constraints
25.4.3 User Acceptance and Trust
25.5 Future Directions and Innovations
25.5.1 Enhanced Multimodal AI Systems
25.5.2 Edge AI for Low-Power Devices
25.5.3 AI-Enhanced Augmented Reality (AR)
25.5.4 Human-AI Collaboration
25.6 Conclusion
References
26. A New Era in Vision Care: Overview of AI Transforming the World of Ophthalmology
Manoj Kumar Pandey, Sourabh Tiwari, Vijay Mohan Shrimal, Gautam Kumar and Naresh Kumar Kar
26.1 Introduction
26.2 Evolution of AI in Ophthalmology
26.3 Related Work
26.4 Present AI Technologies in Ophthalmology
26.4.1 AI for Retinal Diseases
26.4.2 AI in Diagnosis and Screening of Glaucoma Detection
26.4.3 AI in Diagnosis and Screening of Cataract Detection
26.4.4 AI in Personalized Treatment Plans Using AI Algorithms and Monitoring
26.4.5 AI in Surgical Robotics for Eye Care
26.4.6 AI-Enabled Monitoring Devices
26.4.7 AI in OCT and Fundus Imaging
26.4.8 Automated Image Analysis for Precision
26.5 Benefits of AI in Ophthalmology
26.5.1 Improved Accuracy and Early Diagnosis
26.5.2 Accessibility of Vision Care in Underserved Regions
26.5.3 Cost-Effectiveness and Efficiency in Clinical Workflows
26.5.4 Reduction in Patient Wait Times and Better Patient Outcomes
26.6 Discussion and Conclusion
References
27. Creating Synthetic Images for Improved Diagnostics
Kalyan Nagaraj, Amulyashree Sridhar, Shambhavi B. R. and Sindhu K.
27.1 Introduction
27.1.1 Background
27.1.2 Use of AI in Healthcare
27.1.3 AI Advancements in Imaging
27.2 Understanding AI-Generated Imaging
27.2.1 Defining Synthetic Realities
27.2.2 How AI-Generated Imaging Works
27.2.2.1 Generative Adversarial Networks (GANs)
27.2.2.2 Variational Autoencoders (VAEs)
27.2.2.3 Diffusion Models
27.2.2.4 Convolutional Neural Networks (CNNs) in Deep Learning
27.2.2.5 Impact on Medical Diagnostics
27.2.3 Current Applications
27.3 Designed Approach
27.3.1 Research Design
27.3.1.1 Phase 1: Data Preparation
27.3.1.2 Phase 2: Model Design
27.3.1.3 Phase 3: Training and Validation
27.3.1.4 Phase 4: Evaluation and Deployment
27.3.2 Objective
27.3.3 Data Collection
27.3.3.1 Data Sources
27.3.3.2 Ethical Considerations
27.3.4 AI Model Development
27.3.4.1 Model Selection
27.3.4.2 Training and Validation
27.3.4.3 GAN Training for Retinal Fundus Images
27.3.5 Comparison of GAN-Generated and Original Images
27.4 Benefits and Limitations
27.4.1 Advantages of AI-Generated Imaging
27.4.2 Limitations and Challenges
27.5 Future Prospects
27.5.1 Innovations on the Horizon
27.5.2 Integration into Clinical Practice
27.6 Conclusion
References
28. Enhancing the Accuracy of Ophthalmic Models
Devikanniga Devarajan
28.1 Introduction
28.2 Ophthalmic Datasets
28.2.1 Importance of Synthetic Data in Ophthalmology
28.2.2 Challenges in Obtaining Sufficient and Diverse Ophthalmic Data
28.2.3 Benefits Using Synthetic Data for Training
28.3 Background of Ophthalmic Models
28.4 GANs for Ophthalmic Disease Prediction
28.4.1 Ophthalmic Disease and GAN
28.4.1.1 Strabismus
28.4.1.2 Omni-Diabetic Retinopathy
28.4.1.3 Glaucoma
28.4.2 Techniques for Improving GAN Accuracy
28.4.2.1 Image Conditioning
28.4.2.2 Data Augmentation
28.4.2.3 Adversarial Training
28.4.2.4 Transfer Learning
28.4.3 Challenges in Predicting Ophthalmic Images from GANs
28.5 Transfer Learning in Ophthalmology
28.5.1 Applications of Transfer Learning in Ophthalmology
28.5.2 Training TL Models with GAN-Generated Ophthalmic Datasets
28.5.3 Benefits and Challenges of Using Synthetic Data for Training in TL Models
28.5.4 Case Studies on Ophthalmic Imaging Using GAN with Transfer Learning
28.5.4.1 Diabetic Retinopathy Detection
28.5.4.2 Glaucoma Screening
28.5.4.3 Age-Related Macular Degeneration
28.5.5 Analysis of Transfer Learning Architectures in GAN-Generated Ophthalmic Images
28.5.6 Fine-Tuning Pretrained Models
28.5.6.1 InceptionNet Fine-Tuning
28.5.6.2 VGG16 and VGG19 Fine-Tuning
28.6 Evaluation Metrics for Ophthalmic Models
28.6.1 Precision and Recall
28.6.2 Area Under Curve (AUC)
28.7 Parameter Tuning Techniques
28.7.1 Grid Search
28.7.2 Random Search
28.7.3 Bayesian Optimization
References
29. Predicting Treatment Outcomes with Generative AdversarialbNetworks
Priyanka Devi, Mukhtiar Singh and Shivam Sharma
29.1 Introduction
29.2 Objective of This Chapter
29.3 Abstract
29.4 Related Work
29.5 Materials
29.6 Treatment Effect Prediction
29.7 Automated Radiation Therapy
29.8 Conclusion
References
30. GAN-Driven Approaches for Cross-Modality Image Synthesis
Pratik Patel, Shweta Solanki, Gandla Shivakanth and Dharmendra Kumar Dubey
30.1 Introduction
30.2 Literature Review
References
31. AI for Low-Resource Settings and Assistive Technologies: Utilization of GANs for the Generation of Synthetic Data in Resource-Constrained Contexts When Acquiring Extensive Datasets Poses a Barrier
Manjunath H., Sunil R. and Shivakumar
31.1 Introduction
31.2 Literature Review
31.2.1 Research Paper Survey
31.2.2 Existing System Methodologies
31.2.2.1 Traditional Data Augmentation
31.2.2.2 Over Sampling and Under Sampling for Imbalanced Data
31.2.2.3 Model Reuse and Pre Initialized Network
31.3 Proposed System
31.3.1 Dataset
31.3.2 Architecture
31.3.3 Algorithm
31.4 Results and Analysis
31.4.1 Generated Images
31.4.2 Observation and Analysis
31.4.2.1 GAN Training and Loss Comparison
31.4.2.2 Observations
31.4.2.3 Performance and Feature Analysis
31.4.2.4 Tables
31.5 Conclusion
References
32. AI-Enabled Personalized Medicine and Patient-Specific Monitoring
Vinutha N., Bahubali Shiragapur and Chethan K. Murthy
32.1 Introduction
32.1.1 Types of Retinal Disorder
32.1.1.1 Diabetic Retinopathy (DR)
32.1.1.2 Myopia
32.1.1.3 Glaucoma
32.1.2 Imaging Data to Identify Retinal Disorders
32.1.2.1 Fundus Photography
32.1.2.2 Optical Coherence Tomography
32.1.2.3 Optical Coherence Tomography Angiography
32.1.3 AI Applications in Ophthalmology
32.1.3.1 Disease Detection, Diagnosis and Treatment Planning
32.1.3.2 Patient Management and Follow-Up Care
32.1.4 Benefits of Personalized Medicine
32.1.4.1 Enhanced Diagnostic Accuracy
32.1.4.2 Personalized Treatment Plans
32.1.4.3 Genomics in Precision Ophthalmology
32.2 Different Techniques to Improve Precision Medicine
32.2.1 Genomic Medicine and Digital Twin
32.2.2 Generative Models
32.2.3 Explainable AI (XAI) in Precision Medicine
32.2.3.1 Grad-CAM (Gradient-Weighted Class Activation Mapping)
32.2.3.2 SHAP (SHapley Additive exPlanations)
32.2.3.3 LIME (Local Interpretable Model-Agnostic Explanations)
32.2.4 Transformer-Based Learning Framework
32.2.4.1 Data Integration and Embedding
32.2.4.2 Transformer Encoder
32.2.4.3 Prediction and Decision Support
32.2.4.4 Drug Response Prediction
32.2.5 Transformer Variants and Empirical Evidence
32.2.5.1 BERT-Based Models for Clinical Text Processing
32.2.5.2 Vision Transformers (ViTs)
32.3 Conclusions
References
33. The Challenges of Implementing GANs
Hashmat Fida, Aadil Ferooz, Anandaraj, S. P., Ashaq Hussain Bhat and Seema Singh
33.1 Introduction
33.1.1 Overview of Generative Adversarial Networks (GANs)
33.1.1.1 The Architecture of GANs
33.1.1.2 How GANs Learn: The Adversarial Process
33.1.1.3 Applications of GANs
33.1.1.4 Challenges and Limitations
33.1.2 Significance of GANs in Artificial Intelligence and Healthcare
33.1.2.1 Impact in Artificial Intelligence
33.1.2.2 Revolutionizing Healthcare
33.1.3 Relevance of GANs in Ophthalmology
33.1.3.1 Image Enhancement for Improved Diagnosis
33.1.3.2 Synthetic Data for Rare Eye Diseases
33.1.3.3 Automated Diagnosis and AI-Assisted Screening
33.1.4 Key Challenges in GAN Implementation
33.1.4.1 High Data Requirements
33.1.4.2 Training Instability
33.1.4.3 Mode Collapse
33.1.4.4 Computational Complexity
33.1.4.5 Ethical and Bias Issues
33.2 Literature Survey
33.3 Data Challenges in GANs
33.3.1 High Data Requirements in GANs
33.3.1.1 The Need for Large and Diverse Datasets
33.3.2 Challenges in Medical Imaging Datasets
33.3.2.1 Privacy and Ethical Concerns
33.3.2.2 Data Scarcity in Rare Diseases
33.3.2.3 Imbalanced Datasets
33.3.3 Labeling and Annotation Challenges
33.3.4 Solutions to Overcome Data Challenges
33.3.4.1 Data Augmentation
33.3.4.2 Synthetic Data Generation Using GANs
33.3.4.3 Federated Learning for Privacy-Preserving Data Access
33.3.4.4 Transfer Learning and Domain Adaptation
33.3.4.5 Ethical AI Frameworks and Fairness-Aware GANs
33.4 Training Challenges in GANs
33.4.1 Instability in Adversarial Training
33.4.1.1 Non-Convergence due to Unstable Gradients
33.4.1.2 Mode Collapse and Overfitting Problems
33.4.2 Solutions to Instability in GAN Training
33.4.2.1 Advanced Loss Functions
33.4.2.2 Wasserstein Loss (WGAN)
33.4.2.3 Hinge Loss
33.4.2.4 Least Squares Loss (LSGAN)
33.4.3 Architectural Improvements
33.4.4 Optimization Techniques
33.4.4.1 Gradient Penalty
33.4.4.2 Spectral Normalization
33.4.4.3 Batch Normalization
33.5 Mode Collapse and Output Diversity
33.5.1 Why Some GANs Generate Repetitive Outputs
33.5.2 Impact on Medical Image Applications
33.5.3 Solutions to Mode Collapse
33.5.3.1 Minibatch Discrimination – Encouraging Diverse Output
33.5.3.2 Unrolled GANs – Stabilizing Training by Looking Ahead
33.5.3.3 Hybrid Architectures – Using GANs and VAEs to Enhance Diversity
33.6 Ethical and Bias Challenges in GANs
33.6.1 Bias in Training Data and Its Consequences
33.6.2 Case Studies of Bias in Medical AI
33.6.3 Mitigation Strategies for Bias in GANs
33.6.3.1 Fairness-Aware GANs – Algorithmic Fairness Techniques
33.6.3.2 Adversarial Debiasing – Using Adversarial Training to Reduce Bias
33.7 Future Directions and Conclusion
33.7.1 Emerging Trends in GAN Research
33.7.2 The Role of GANs in Ophthalmology and Future AI-Driven Healthcare
33.7.3 Open Challenges and Ongoing Research Areas
33.7.4 Final Thoughts on Overcoming GAN Implementation Hurdles
References
34. Ethics and Regulations in AI-Driven Ophthalmology
M.K. Jayanthi Kannan, Shree Nee Thirumalai Ramesh and K. Mariyappan
34.1 Introduction
34.2 Ethical Challenges in AI-Driven Ophthalmology
34.2.1 Data Privacy and Security
34.2.2 Algorithmic Bias and Equity
34.2.3 Accountability and Transparency
34.2.4 Patient Consent and Autonomy
34.2.5 Human Role in Decision-Making
34.2.6 Equity and Access to Care
34.3 Regulatory Frameworks for AI in Ophthalmology
34.3.1 Global Regulatory Approaches
34.3.2 Challenges in Regulation
34.4 Regulatory Progress
34.4.1 Regulatory Perspectives under FDA’s 2023 AI/ML Framework
34.4.2 EU AI Act (2024)
34.4.3 ‘NMPA’ the Green Channel of China
34.5 AI Ethics Principles Proposed by Intergovernmental Organizations and Countries
34.6 Conclusion
References
35. AI in Preventive Eye Care
Rajive Gandhi C., Reshmma V., Priyadarshini B., Darshana B., Tarani V. and A. K. Jayanthy
35.1 Introduction
35.1.1 Common Eye Diseases and Their Effect
35.1.1.1 Diabetic Retinopathy
35.1.1.2 Age-Specific Macular Degeneration (AMD)
35.1.1.3 Glaucoma
35.2 Artificial Intelligence-Based Technologies for Detecting Eye Diseases Through Retinal Imaging
35.2.1 Age-Related Macular Degeneration (AMD)
35.2.2 Generative Adversarial Networks (GANs) for Retinal Image Synthesis
35.2.2.1 Applications of GANs in Retinal Imaging?
35.2.3 Transformer Models for Eye Disease Detection
35.2.3.1 Why Are Transformers Useful in Retinal Imaging?
35.2.3.2 Examples of Real-World Applications of Transformers in Eye Care
35.3 Disease-Specific AI Applications
35.3.1 Evolution of AI in Eye Disease Diagnosis and Treatment
35.3.1.1 AI for Early Detection of Disease
35.3.1.2 Identifying Eye Disease with AI
35.3.1.3 AI in Disease Progress Prediction
35.3.1.4 AI in Treatment and Management
35.4 Use of AI in Teleophthalmology to Increase the Reach of Eye Care Services
35.4.1 Challenges in Implementing Teleophthalmology
35.5 Rationale of AI in Preventive Eye Care: Addressing Challenges and Ethical Issues
35.5.1 Bias in AI
35.5.2 Data Privacy and Security in AI-Driven Eye Care
35.5.3 Regulatory and Clinical Validation Challenges
35.5.4 Ethical Concerns
35.5.5 Addressing Socioeconomic Disparities
35.5.6 Transparency in AI Decisions
35.5.7 Interdisciplinary Collaboration
35.5.8 Global Implications with Tailored AI Solutions
35.5.9 Culture of Continuous Improvement in AI Systems
35.6 Future Directions and Conclusion: Emerging AI Trends and Their Potential
35.6.1 Genomics and AI Integration
35.6.2 Lifestyle and Environmental Factors
35.6.3 Optimizing Treatment Responses
35.6.4 AI-Powered AR for Vision Restoration
35.6.5 AI-Improved Smart Glasses
35.6.6 Real-Time Object Recognition
35.6.7 Therapeutic AR in Vision Training
35.6.8 Empower XAI with Trust
35.6.9 Understanding AI Predictions
35.6.10 Creating Clinical Confidence
35.6.11 Creating Trust of the Patients
35.7 Conclusion
References
36. Collaborative AI in Smart Eye Care: Integration of AI, IoT, and Wearable Technologies for Eye Health Monitoring
Ankush Raj and Amit Kumar
36.1 Introduction
36.2 Related Work
36.2.1 AI in Ophthalmology
36.2.2 Eye-Tracking and Autism Diagnosis
36.2.3 IoT and Wearables in Eye Care
36.3 Methodology
36.3.1 Data Collection
36.3.2 Machine Learning Models
36.3.2.1 Machine Learning Models for ASD Classification
36.3.2.2 Justification for Model Selection
36.3.3 Model Training and Evaluation
36.4 Results and Discussion
36.4.1 Logistic Regression
36.4.2 Random Forest Classifier
36.4.3 Decision Tree Classifier
36.4.4 Comparative Analysis of Classification Models
36.5 Conclusion
References
37. Exploring Adaptive Multimodal Generative AI for Personalized Content Creation
N. Sivakumar, G. Manivasagam, S. Mohamed Sulaiman, Pavithra K., Daljeet Kaur, Thiruvenkadam Thangavel and S. Boopathiraja
37.1 Introduction
37.1.1 Limitations of Current Generative Models in Personalization
37.2 Understanding Multimodal Generative AI
37.2.1 How Does Multimodal Synthesis Impact Healthcare and Patient Engagement
37.2.2 Applications in Ophthalmic Diagnostics and Patient Education
37.3 Adaptive Framework Design: AMGAI
37.3.1 Architecture Overview and Core Modules
37.4 Context-Aware Generative Layers (CAGL)
37.4.1 Various Technical Strategies are Used to Operate
This Dynamic Adjustment
37.4.2 Personalization Based on User Intent, Medical History, and Environment
37.4.3 Use Cases in Personalized Vision Care and Communication
37.5 Feedback-Driven Evolutionary Algorithm (FDEA)
37.5.1 Incorporation of Reinforcement Learning for Continuous Adaptation
37.5.2 Enhancing Content Coherence, Creativity, and Relevance
37.6 Personalized Content for Ophthalmic Consultations, Reports, and Education
37.6.1 Generative AI in Creating Tailored Rehabilitation Plans and Awareness Materials
37.6.2 Enhancing Accessibility Through Multilingual and Multimodal Outputs
37.7 Performance Evaluation
37.7.1 Comparison with Traditional Generative Models
37.7.2 Results from Early Experiments and Case Studies
37.8 Challenges and Ethical Considerations
37.8.1 Data Privacy and Patient Consent in Adaptive AI Systems
37.8.2 Bias Mitigation and Responsible Personalization
37.8.3 Ensuring Clinical Accuracy and User Trust
37.9 Future Directions
37.9.1 Expansion to Real-Time Interactive Generative Assistants in Ophthalmology 37.9.2 Research Opportunities in Adaptive Generative AI
37.10 Conclusion
References
38. Self-Supervised Learning for Early Detection of Diabetic Retinopathy: A Generative AI Perspective
Kiruthika S., Naveenbalaji Gowthaman, S. Sathya Bama and Hanisha Vijayakumar
38.1 Introduction
38.2 Research Landscape on DR Diagnosis
38.3 Framework for DR
38.4 Contrastive Learning for Retinopathy
38.4.1 Pretraining with Contrastive Learning
38.4.2 Fine-Tuning with Minimal Labels
38.4.3 Self-Supervised Diabetic Retinopathy Detection
38.4.4 Attention-Based Interpretability
38.5 Results and Discussion
38.5.1 Performance Evaluation
38.5.2 Comparison with Existing Models
38.5.3 Statistical Validation and Robustness (Enhancement)
38.6 Conclusions and Future Recommendations
References
39. Future Directions: From Generative AI to General AI in Ophthalmic Care
Pooja Dixit, Pramod Singh Rathore and Dharmendra Kumar Dubey
39.1 Introduction
39.1.1 Current Role of Generative AI in Ophthalmology
39.1.2 General AI in Ophthalmology: Vision for the Future
39.1.3 Challenges in the Transition
39.2 Literature Survey
39.3 Generative AI Applications in Ophthalmic Imaging
39.4 Towards General AI: Concept and Vision
39.4.1 Difference between Generative AI and General AI
39.4.2 Role of Multi-Modal Integration and Adaptive Learning
39.5 Technical Challenges and Research Gaps
39.5.1 Challenge of Data Integration
39.5.2 Problem of Generalizability
39.5.3 Interpretability and Explainability
39.5.4 Ethical and Clinical Deployment Issues
39.6 Proposed Framework for General AI in Ophthalmology
39.7 Future Scope and Innovation Pathways
Conclusion
References
Index

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