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Submit a ProposalBrain-Inspired Artificial Intelligence and Neuromorphic Computing in Healthcare
 | Edited by Jyotir Moy Chatterjee
Series: Machine Learning in Biomedical Science and Healthcare Informatics Copyright: 2026 | Expected Pub Date:5/30/2026 ISBN: 9781394395446 | Hardcover | 612 pages |
One Line Description
Discover how brain-inspired, neuromorphic AI is shattering the limits of classical computing to revolutionize medicine by delivering the real-time, adaptive intelligence needed to transform everything from robotic surgeries and precision diagnostics to the future of patient care.
Audience
Academics, researchers, healthcare professionals, and policymakers across artificial intelligence, biomedical engineering, computational neuroscience, and healthcare informatics, seeking insights into brain-inspired AI and neuromorphic systems.
Academics, researchers, healthcare professionals, and policymakers across artificial intelligence, biomedical engineering, computational neuroscience, and healthcare informatics, seeking insights into brain-inspired AI and neuromorphic systems.
Description
Current AI technologies often rely on classical computational models, which may struggle to replicate the complex, adaptive learning of the brain. Neuromorphic computing overcomes these limitations, processing medical data faster and more intelligently, which is particularly valuable in high-stakes environments like healthcare. Brain-inspired algorithms can significantly improve disease diagnosis, predictive analytics, robotic surgeries, patient monitoring, and neuroprosthetics by adapting in real time, like the human brain itself. This book focuses on the integration of neuromorphic computing into healthcare. Neuromorphic computing, which uses brain-like algorithms and architectures, has the potential to revolutionize healthcare by enhancing real-time diagnostics, improving patient outcomes, enabling personalized medicine, and transforming healthcare systems into more adaptive and efficient entities. The book explores the potential applications of neuromorphic computing in healthcare, examining topics like brain-computer interfaces, AI-enhanced diagnostics, decision support systems, personalized medicine, biomedical engineering, and the ethical issues that arise with the deployment of AI in medicine. This comprehensive exploration provides a detailed understanding of how brain-inspired AI and neuromorphic technologies are set to transform healthcare. It serves as a guide for researchers, healthcare professionals, and policymakers with a focus on technical advancements, practical implementations, and future trends in neuromorphic computing applied to the healthcare field.
Readers will find the volume:
• Introduces the fundamental principles of neuromorphic computing and its contrast with traditional AI;
• Explores real-world applications in disease diagnosis, robotic surgeries, clinical decision-making, and personalized healthcare;
• Discusses technical challenges like data scalability, hardware efficiency, and integrating systems into existing healthcare workflows;
• Addresses ethical and legal considerations in deploying neuromorphic AI in healthcare, including patient data privacy and fairness in decision-making;
• Looks to the future of healthcare innovation, driven by neuromorphic computing, with applications in neuroprosthetics and health monitoring systems.
Back to Top
Current AI technologies often rely on classical computational models, which may struggle to replicate the complex, adaptive learning of the brain. Neuromorphic computing overcomes these limitations, processing medical data faster and more intelligently, which is particularly valuable in high-stakes environments like healthcare. Brain-inspired algorithms can significantly improve disease diagnosis, predictive analytics, robotic surgeries, patient monitoring, and neuroprosthetics by adapting in real time, like the human brain itself. This book focuses on the integration of neuromorphic computing into healthcare. Neuromorphic computing, which uses brain-like algorithms and architectures, has the potential to revolutionize healthcare by enhancing real-time diagnostics, improving patient outcomes, enabling personalized medicine, and transforming healthcare systems into more adaptive and efficient entities. The book explores the potential applications of neuromorphic computing in healthcare, examining topics like brain-computer interfaces, AI-enhanced diagnostics, decision support systems, personalized medicine, biomedical engineering, and the ethical issues that arise with the deployment of AI in medicine. This comprehensive exploration provides a detailed understanding of how brain-inspired AI and neuromorphic technologies are set to transform healthcare. It serves as a guide for researchers, healthcare professionals, and policymakers with a focus on technical advancements, practical implementations, and future trends in neuromorphic computing applied to the healthcare field.
Readers will find the volume:
• Introduces the fundamental principles of neuromorphic computing and its contrast with traditional AI;
• Explores real-world applications in disease diagnosis, robotic surgeries, clinical decision-making, and personalized healthcare;
• Discusses technical challenges like data scalability, hardware efficiency, and integrating systems into existing healthcare workflows;
• Addresses ethical and legal considerations in deploying neuromorphic AI in healthcare, including patient data privacy and fairness in decision-making;
• Looks to the future of healthcare innovation, driven by neuromorphic computing, with applications in neuroprosthetics and health monitoring systems.
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Author / Editor Details
Jyotir Moy Chatterjee is an Assistant Professor in the Department of Computer Science and Engineering, Graphic Era University, Dehradun, India, and an Assistant Professor in the Department of Information Technology at the Lord Buddha Education Foundation, affiliated with the Asia Pacific University of Technology and Innovation, Malaysia. He has more than 100 publications to his credit, including book chapters and articles in international journals and conferences. He has edited multiple volumes. His research interests focus on machine learning and deep learning.
Jyotir Moy Chatterjee is an Assistant Professor in the Department of Computer Science and Engineering, Graphic Era University, Dehradun, India, and an Assistant Professor in the Department of Information Technology at the Lord Buddha Education Foundation, affiliated with the Asia Pacific University of Technology and Innovation, Malaysia. He has more than 100 publications to his credit, including book chapters and articles in international journals and conferences. He has edited multiple volumes. His research interests focus on machine learning and deep learning.
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Table of Contents
Preface
Acknowledgement
Part I: Foundations and Emerging Paradigms in Neuromorphic Healthcare
1. Neuromorphic Computing and Multidisciplinary Healthcare
Teams
Archana Chhabra, Harpreet Kaur and Deepika Ghai
1.1 Overview of Healthcare Neuromorphic Computing
1.1.1 Definition and Main Principles of Neuromorphic Computing
1.1.2 Neuromorphic Computing’s Evolution
1.1.3 Importance of Neuromorphic Computing in Healthcare
1.2 Fundamental Ideas in Neuromorphic Computing for Medical Applications
1.2.1 Neuromorphic Hardware and Architecture
1.2.1.1 Key Hardware Platforms
1.2.2 Spiking Neural Networks (SNNs) in Healthcare
1.2.3 Energy-Efficient AI for Medical Applications
1.2.3.1 Low-Power Computing for Wearable Devices
1.2.4 Role of Neuromorphic Computing in Edge AI for Healthcare
1.3 Applications of Neuromorphic Computing in Healthcare
1.3.1 Medical Imaging and Diagnostics
1.3.2 Wearable Consumer Electronics for Health Monitoring
1.3.3 Personalized Medicine and Treatment Optimization
1.4 Role of Multidisciplinary Healthcare Teams
1.4.1 Composition of Multidisciplinary Teams
1.4.2 Collaboration Challenges and Solutions in AI-Driven
Healthcare
1.4.2.1 Challenges
1.4.2.2 Solutions
1.4.3 A Case Study of a Successful Interdisciplinary Healthcare Project
1.5 Conclusion, Future Perspectives, and Research Challenges
1.5.1 Future Perspectives
1.5.1.1 Advancements in Neuromorphic Hardware for Real-Time Healthcare Applications
1.5.1.2 Integration with Edge AI, IoT, and Cloud-Based Healthcare Systems
1.5.1.3 Potential Impact on Remote Patient Monitoring and Telemedicine
1.5.2 Research Challenges in Clinical Implementation and Scalability
References
2. Neuromorphic AI in Medical Diagnostics
S. Mohanraj and R. Sujatha
2.1 Introduction
2.1.1 Overview of AI in Medical Diagnostics
2.1.2 Limitations of Traditional Deep Learning Models in Medical Diagnostics
2.1.3 Introduction to Neuromorphic AI and Its Significance in Healthcare
2.2 Fundamentals of Neuromorphic AI
2.2.1 Definition and Principles
2.2.2 Spiking Neural Networks (SNNs) and Event-Based Processing
2.2.3 Comparison with Conventional Deep Learning Methods
2.2.4 Advantages of Neuromorphic Computing
2.3 Applications in Medical Diagnostics
2.3.1 Cancer Screening
2.3.2 Cardiovascular Risk Stratification
2.3.3 Diagnosis of Neurological Diseases
2.3.4 Personalized Medicine
2.4 Technological Innovations and Hardware Implementations
2.4.1 Interpretability and Data Privacy
2.4.2 Neuromorphic Hardware Innovations
2.4.3 Comparing Neuromorphic AI With Traditional Deep Learning
2.5 Interpretability, Ethics, and Regulatory Considerations
2.5.1 Interpretability Challenges in Neuromorphic AI
2.5.2 Data Privacy and Compliance
2.5.3 Regulatory Challenges and Future Directions
2.6 Comparative Analysis: Neuromorphic AI vs. Traditional Deep Learning
2.7 Conclusion and Future Prospects
2.7.1 Summary of Key Findings
2.7.2 Potential of Neuromorphic AI in Next-Gen Medicine
2.7.3 Future Research Directions and Emerging Trends
Conclusion
References
3. Addressing Scalability and Reliability in Neuromorphic
Healthcare
Manas Kumar Yogi, K. V. V. Subba Rao, Shanmukha Mahathi Chinta and S. Soha Tabassum
3.1 Introduction to Neuromorphic Healthcare
3.1.1 Overview of Neuromorphic Systems in Healthcare
3.1.2 The Role of Scalability and Reliability in Neuromorphic Healthcare
3.1.3 A New Standard for Intelligent Healthcare
3.2 Overcoming Scalability and Reliability Challenges in Neuromorphic Healthcare
3.2.1 Scalability Challenges in Neuromorphic Healthcare
3.2.2 Reliability Challenges in Neuromorphic Healthcare
3.2.3 Balancing Performance, Efficiency, and Accuracy
3.2.4 Design Guidelines for Robust Models for Scalable and Reliable Neuromorphic Systems
3.3 Construction of Proposed Model
3.3.1 Implementation of the SBFL Model
3.3.2 Training, Optimization, and Algorithm Details
3.3.3 Results and Performance Evaluation
3.3.4 Results and Discussion
3.3.5 Statistical Validation
3.3.6 Case Study: Real-World Deployment in a Hospital Network
3.4 System Optimization and Performance Enhancements
3.4.1 Cutting-Edge Optimization Techniques
3.4.2 Precision Performance Tuning
3.4.3 Real-World Adaptability and Resilience
3.4.4 Futuristic Enhancements and Next Steps
3.5 Future Enhancements of the SBFL Model
3.5.1 Adaptive Thresholding for Dynamic Learning
3.5.2 Neuromorphic Hardware Accelerators
3.5.3 Bio-Plausible Learning Rules
3.5.4 Hierarchical SNN Architectures
3.5.5 Hybrid Quantum-SNN Models
3.5.6 Self-Adaptive and Multi-Agent SBFL
3.5.7 Lifelong Learning and Holographic Memory
3.6 Conclusion
3.6.1 Significant Takeaways
3.6.2 Closing Reflections
References
4. A Comprehensive Review of Brain-Computer Interfaces:
Current Research Trends in Person-Centric Healthcare and Ambient Assisted Living
P. Nagaraj, Josephine Selle Jeyanathan, A. Lakshmi, V. Muneeswaran and K. Muthamil Sudar
4.1 Introduction to Brain-Computer Interface (BCI)
4.1.1 Definition and Overview
4.1.2 Significance of BCI Technology
4.1.3 Schematic Architecture of BCI
4.1.4 Benefits of BCI
4.2 Principles of Brain Signal Acquisition
4.2.1 Electroencephalography
4.2.2 Invasive Methods for Brain Signal Acquisition
4.3 Feature Extraction Methods in BCI
4.3.1 Time-Domain Features
4.3.2 Frequency-Domain Features
4.3.3 Time-Frequency Domain Features
4.4 BCI Modalities and Technologies
4.4.1 Non-Invasive BCI Technology
4.4.1.1 Non–Invasive EEG Electrode Materials
4.4.1.2 Durability
4.4.1.3 The Quality of Biocompatibility
4.4.2 Invasive BCI Technology
4.4.2.1 Classification of Invasive BCI
4.5 Applications of BCI
4.5.1 Healthcare Applications
4.5.2 Rehabilitation Applications
4.5.3 Communication and Assistive Technology
4.6 Challenges and Future Directions
4.6.1 Signal Quality
4.6.2 Adaptability and Cost Effectiveness
4.6.3 Safety and Ethical Factors
4.6.4 Future Directions
References
5. Neuromorphic Systems for BCI and Neuroprosthetics
T.S. Suganya and Midhun Ramesh
5.1 Introduction
5.2 What are Neuromorphic Systems?
5.3 How Brain-Computer Interfaces (BCIs) Work
5.4 What are Neuroprosthetics?
5.5 Neuromorphic Hardware for BCIs and Neuroprosthetics
5.6 Signal Acquisition and Processing in BCI Systems
5.7 Applications of Neuromorphic Systems in BCI and Neuroprosthetics
5.8 Challenges and Future Directions
5.9 Types of Neuromorphic Architectures and Their Role in BCIs and Neuroprosthetics
5.10 Comparative Study: Neuromorphic vs Traditional Systems in BCIs
5.11 Case Studies and Real-World Implementations
5.12 Ethical, Legal, and Societal Implications of Neuromorphic
BCIs
5.13 Interdisciplinary Research and Future Scope
5.14 Educational and Skill Development Aspects
5.15 Integration with Other Emerging Technologies
5.16 User Experience and Accessibility in Neuromorphic BCIs
5.17 Safety and Reliability in Neuromorphic Systems
5.18 Economic Impact and Market Trends
5.19 Regulatory and Standardization Challenges for Neuromorphic BCIs
Conclusion
Bibliography
6. Neuromorphic Computing for Wearable Health Devices
Wasswa Shafik
6.1 Introduction
6.1.1 Definition and Principles
6.1.2 Historical Development
6.1.3 Application Overview in Health Devices
6.2 Wearable Health Devices
6.2.1 Types and Features
6.2.2 Importance of Neuromorphic Computing in Wearable Health Devices
6.2.2.1 Enhanced Processing Efficiency
6.2.2.2 Low Power Consumption
6.3 Challenges and Limitations of Current Computing Technologies
6.3.1 Processing Speed
6.3.2 Energy Efficiency
6.4 Neuromorphic Computing Architectures
6.4.1 Spiking Neural Networks
6.4.2 Memristor-Based Architectures
6.5 Machine Learning Algorithms for Health Monitoring
6.5.1 Supervised and Unsupervised Learning
6.5.2 Deep Learning Models
6.6 Applications of Neuromorphic Computing in Wearable Health Devices
6.6.1 Real-Time Health Monitoring
6.6.2 Disease Detection and Diagnosis
6.7 Future Directions and Research Opportunities
6.7.1 Integration with Internet of Things (IoT)
6.7.2 Biocompatible Materials for Wearables
6.7.3 Advancements in Neuromorphic Hardware
6.7.4 Personalized Healthcare
6.8 Conclusion
References
7. Neuro-Inspired Advances in Imaging and Diagnostics
Ajay Sharma and Arun Malik
7.1 Introduction
7.1.1 Neuroscience Principles in Imaging
7.2 Bio-Inspired Computational Models
7.3 Neural Mechanisms in Vision and Imaging
7.4 Advancements in Imaging Techniques
7.5 Neuro-Inspired Diagnostics
7.5.1 Applications in Cancer Detection, Cardiology, and Ophthalmology
7.6 Clinical Applications and Challenges of Neuro‑Inspired
Imaging in Healthcare
7.7 Ethical, Regulatory, and Practical Challenges in Implementation
Conclusion
References
8. Harnessing Brain-Inspired AI for Healthcare’s Digital Future
Bittoo Kumar Sharma
8.1 Overview of AI in Healthcare Digital Transformation
8.1.1 The Role of AI in Digital Healthcare Transformation
8.1.2 AI in Healthcare Operations and Management
8.1.3 AI in Remote and Virtual Healthcare
8.1.4 Challenges and Ethical Considerations in AI-Driven Healthcare
8.1.4.1 Data Privacy and Security Concerns
8.1.4.2 Bias in AI Algorithms
8.1.4.3 The Role of Human Oversight
8.1.5 Future Prospects of AI in Healthcare Digital Transformation
8.2 Limitations of Traditional AI Systems in Healthcare
8.2.1 The Challenges of Traditional AI in Healthcare
8.2.2 Ethical and Regulatory Challenges
8.3 The Paradigm Shift towards Brain-Inspired AI Introduction
8.3.1 How Brain-Inspired AI Works
8.3.1.1 Neuromorphic Computing: Mimicking the Human Brain
8.3.1.2 Cognitive AI: Learning and Adaptability
8.3.2 Applications of Brain-Inspired AI in Healthcare
8.3.2.1 AI for Personalized Diagnostics and Treatment
8.3.2.2 Real-Time Disease Monitoring
8.3.2.3 AI-Powered Robotics in Surgery
8.3.3 Understanding Brain-Inspired AI and Neuromorphic Computing
8.4 Conceptual Foundation of Brain-Inspired AI
8.4.1 Understanding Brain-Inspired AI?
8.4.2 Key Characteristics of Brain-Inspired AI:
8.4.3 Differences between Brain Inspired AI and Traditional AI
8.4.4 Real-World Applications of Brain-Inspired AI
8.4.4.1 Key Applications of Brain-Inspired AI
8.4.4.2 AI in Healthcare: Smarter Diagnosis and Personalized Treatment
8.5 Neuromorphic Computing: Bridging AI and Neuroscience
8.5.1 What is Neuromorphic Computing?
8.5.1.1 Key Features of Neuromorphic Computing
8.5.2 How Neuromorphic Computing Connects AI and Neuroscience
8.5.2.1 Mimicking Biological Neurons and Synapses
8.5.2.2 Real-Time Learning and Adaptability
8.5.2.3 Future Application of Neuromorphic Computing
8.5.3 Core Differences between Traditional AI and Brain-Inspired AI
8.5.4 Biological Neural Networks vs. Artificial Neural Networks
8.6 Uses of AI Inspired by the Brain in Healthcare
8.6.1 Diagnostics and Imaging in Medicine
8.6.2 Early Disease Detection and Predictive Analytics
8.6.3 Individualized Medicine and Therapy Programs
8.6.4 Intelligent Automation and Robotic Surgery
8.6.5 Wearable Technology and Remote Patient Monitoring
8.6.6 Diagnosing Neurological Disorders and Cognitive Health
8.7 Benefits of AI Inspired by the Brain for Digital Healthcare Systems
8.7.1 Making Decisions in Real Time
8.7.2 Efficiency and Low Energy Use
8.7.3 Minimal Data Adaptive Learning
8.7.4 Improved Transparency and Interpretability
8.7.5 Scalability in Healthcare Ecosystems
8.8 Challenges and Ethical Considerations in Brain-Inspired AI for Healthcare
8.8.1 Security and Privacy of Data
8.8.1.1 Neuromorphic AI in Healthcare: Ethical Frameworks
8.8.1.2 Complexity of Computation and Hardware Restrictions
8.8.1.3 Describe Aptitude and Bias in Brain-Inspired Models
8.8.2 Adherence to Regulations in Brain Medical Applications
8.8.3 AI Inspired by the Brain in Medical Imaging Systems
8.8.4 Wearable Medical Technology with Neuromorphic Sensors
8.8.5 Healthcare Predictive Systems for the Management of Chronic Illnesses
8.8.6 Brain-Machine Interfaces for the Treatment of Paralysis
8.9 Future Directions of Brain-Inspired AI in Healthcare
8.9.1 Collaboration with IoT and Quantum Computing
8.9.2 Intelligible and Reliable AI Models
8.9.3 Developments in Neuromorphic Hardware
8.9.4 AI-Powered Genomics and Drug Discovery
8.9.5 The Ethical and Policy Framework for Next-Gen AI
Conclusion
Recommendations
Bibliography
Part II: Ethical, Behavioral, and Application-Specific Innovations
9. Ethical and Legal Challenges in Using Neuromorphic AI for Diagnosing and Treating Childhood Trauma
Navonita Mallick, Shashwata Sahu and Hiranmaya Nanda
9.1 Introduction
9.2 Research Objectives and Methodology
9.3 Neuromorphic AI in Healthcare: A Technical Overview
9.4 Understanding Childhood Tauma Clinical and Ethical Context
9.5 Ethical Challenges in Using Neuromorphic AI for Diagnosing and Treating Childhood Trauma
9.6 Legal Challenges: Accountability, Data Protection, and Global Frameworks
9.7 Comparative Analysis: Global Best Practices and Policy Gaps in Regulating Neuromorphic AI in Healthcare
9.8 Proposed Framework for Ethical and Legal Governance of Neuromorphic AI in Pediatric Mental Health
9.9 Conclusion
References
10. Neuromorphic Computing in Sports: New Age Technology in Transforming Athlete Performance and Rehabilitation
Amitava Pal, Rajkumar M. and Kavita Mathad
Introduction
Objective of the Chapter
Neuromorphic Computing: Concepts and Capabilities
Practical Scenario: Neuromorphic Computing in Action for Athletes
Current Applications in Sports
Game Strategy and Decision-Making Support
Integration with Sports Technology Ecosystem
Future Innovations and Possibilities: Expanded Use Cases and Scenarios
Challenges and Limitations
Implications for Sports Science and Healthcare
Discussion Points
Conclusion
Declaration
Abbreviations
References
11. An Intelligent System for Early Mental Health Detection: Integrating Wearables, Social Media, and Neuromorphic
Richa Mathur and Rakesh Roshan
11.1 Introduction
11.2 Literature Review
11.2.1 Comparison of Results from Previous Studies
11.3 Problem Definition
11.4 Methodology
11.4.1 Dataset Description
11.4.1.1 Data Collection Sources
11.4.1.2 Dataset Entries
11.4.2 Data Preprocessing
11.4.3 Platforms and Tools
11.4.4 Algorithms Used
11.5 Proposed Model for Early Detection of Mental Health Issues
11.5.1 Flow Diagram (DFD)
11.5.2 Proposed Workflow Pipeline
11.6 Experimental Results: Accuracy Table
11.7 Ethical Considerations
11.8 Conclusion
References
12. Neuromorphic AI-Driven Personalization and Predictive
Analytics in Digital Health: A Marketing Perspective on Patient Engagement Metrics and Outcomes
Rahul Chauhan and Bhoomi Chauhan
Introduction
Literature Review
Research Methodology
Objectives
Analysis
Conclusion
Acknowledgement Statement
References
13. Building Neuromorphic Systems for Medicine
Devanshi Srivastava and Adarsh Kumar Arya
13.1 Introduction
13.2 Fundamentals of Neuromorphic Computing
13.2.1 Key Principles of Brain-Inspired Computing
13.2.2 Comparison of Neuromorphic Systems for Medicine with Traditional Computing Architectures
13.2.3 Advantages of Neuromorphic Systems for Medical Applications
13.3 Neuromorphic Hardware Platforms
13.3.1 Overview of Existing Neuromorphic Chips and Systems
13.3.2 Specific Hardware Considerations in Neuromorphic Systems
13.3.3 Challenges and Opportunities of Neuromorphic Systems
13.4 Neuromorphic Algorithms for Medical Data Processing
13.4.1 Spiking Neural Networks (SNNs) and Their Applications in Medicine
13.4.2 Learning and Adaptation in Neuromorphic Systems
13.4.3 Real-Time Processing of Complex Medical Data in Neuromorphic Systems
13.5 Applications in Medical Imaging in Neuromorphic Systems
13.5.1 Neuromorphic Approaches to Image Reconstruction and Enhancement
13.5.2 Fast and Efficient Analysis of Medical Images in Neuromorphic Systems
13.5.3 Integration with Existing Medical Imaging Technologies in Neuromorphic Systems
13.6 Neuromorphic Systems for Bio Signal Processing
13.6.1 EEG, ECG, and EMG Signal Analysis Using Neuromorphic Architectures
13.6.2 Real-Time Monitoring and Anomaly Detection
13.7 Neuromorphic Approaches in Drug Discovery and Development
13.7.1 Accelerating Drug Screening Processes in Neuromorphic Systems for Medicine
13.7.2 Predicting Drug Interactions and Side Effects in Neuromorphic Systems for Medicine
13.8 Personalized Medicine and Treatment Planning through Neuromorphic Systems
13.8.1 Adaptive Learning for Patient-Specific Models in Neuromorphic Systems
13.8.2 Real-Time Treatment Optimization in Neuromorphic Systems for Medicine
13.8.3 Integration of Neuromorphic Systems with Electronic Health Records
13.9 Future Directions and Emerging Trends of Neuromorphic Systems for Medicine
13.9.1 Integrating Neuromorphic Systems with Other Emerging Technologies
13.9.2 Potential Breakthroughs in Neurodegenerative Disease Research
13.9.3 Scaling Neuromorphic Systems for Large-Scale Medical Applications
13.10 Conclusion
References
14. Intelligent Neuromorphic Frameworks for Neural Interfaces and Prosthetic Control
Aditya Dayal Tyagi, Himani Tyagi, Setu Garg and Vivek Tomar
14.1 Introduction
14.1.1 Understanding Brain-Computer Interfaces and Neuroprosthetics
14.1.2 The Role of AI in Interpreting Brain Signals
14.1.3 Emergence of Neuromorphic Systems as Brain-Inspired Processors
14.1.4 Relevance to Healthcare and Assistive Technologies
14.2 Biological Inspiration behind Neuromorphic Computing
14.2.1 Fundamentals of Neural Coding and Brain Signal Processing
14.2.2 Spiking Neural Networks (SNNs) and Brain-Like Computation
14.2.3 Comparison with Traditional von Neumann Architectures
14.2.4 Need for Real-Time, Adaptive Signal Processing in BCIs and Neuroprosthetics
14.3 Architecture of Neuromorphic Systems
14.3.1 Overview of Neuromorphic Hardware: IBM TrueNorth, Intel Loihi, SpiNNaker, BrainScaleS
14.3.2 Event-Driven Architecture and Asynchronous Processing
14.3.3 Integration with Bio Signals and Sensors (EEG, EMG, ECoG)
14.4 Neuromorphic Interfaces for Brain-Computer Interaction
14.4.1 Signal Acquisition, Preprocessing, and Encoding into Spike Trains
14.4.2 Real-Time Decoding Using Spiking Neural Networks (SNNs)
14.4.3 Closed-Loop BCI Using Neuromorphic Systems
14.4.4 Case Studies: Motor Imagery Decoding, Cognitive Load Estimation, and More
14.5 Applications in Neuroprosthetics
14.5.1 Prosthetic Limb Control Using BCI and Neuromorphic Processors
14.5.2 Cochlear and Retinal Implants
14.5.3 Adaptive Learning in Neuromorphic Prosthetics
14.5.4 Energy Efficiency and Miniaturization Benefits
14.6 Co-Design of Neuromorphic Hardware and Algorithms
14.6.1 Bio-Signal Friendly Neural Encoding and Learning Rules
14.6.2 On-Chip Learning Mechanisms
14.6.3 Brain-Inspired Adaptation and Plasticity for Personalized Care
14.7 Challenges and Limitations
14.7.1 Signal Variability and Noise in Neural Data
14.7.2 Scalability and Interface Complexity
14.7.3 Ethical and Regulatory Concerns
14.7.4 Integration with Wearable or Implantable Devices
14.8 Future Prospects
14.8.1 Neuromorphic Chips in Embedded and Edge BCIs
14.8.2 Bi-Directional BCIs for Sensory Feedback
14.8.3 Federated Neuromorphic Learning for Multi-User Healthcare Systems
14.8.4 Synergies with Brain Organoids and Digital Twins
14.9 Conclusion
References
Part III: Advanced Models, Diagnostics, and Future Outlook
15. Neuromorphic Innovations in Medical Diagnosis and Imaging
Himani Tyagi, Aditya Dayal Tyagi, Kimmi Verma and Ravi Prakash Chaturvedi
15.1 Introduction
15.2 Foundation of Neuro-Inspired Computing
15.2.1 What is Brain-Inspired Computing
15.2.2 Spiking Neural Networks (SNNs): Core Building Block
15.2.3 Neuromorphic Sensors and Data Acquisition
15.2.4 Neuromorphic Hardware Platforms
15.2.5 Comparison with Conventional AI
15.3 Diagnostic Imaging: Current Landscape
15.3.1 Role of AI in Medical Imaging
15.3.2 Challenges in Conventional AI-Based Imaging
15.3.3 Need for Real-Time, Low-Power Diagnostics
15.3.4 Early Signs of Neuromorphic Adoption
15.4 Neuro-Inspired Techniques in Imaging
15.4.1 Spiking-Based Image Segmentation
15.4.2 Denoising and Image Enhancement
15.4.3 Spiking Attention Models and Transformers
15.4.4 Real-Time, Low-Power Inference
15.4.5 Emerging Use Cases in Clinical Practice
15.5 Comparative Evaluation and Key Insights
15.5.1 Comparative Assessment and Key Findings
15.5.2 Power Consumption and Deployability at the Edge
15.5.3 Real-Time Inference and Latency
15.5.4 Interpretability and Biological Plausibility
15.5.5 Table of Main Takeaways
15.6 Challenges and Limitations
15.6.1 Training Complexity and Optimization Constraints
15.6.2 Hardware Access and Ecosystem Fragmentation
15.6.3 Dataset Incompatibility and Lack of Temporal Benchmarks
15.6.4 Regulatory Complexity and Clinical Novelty
15.6.5 Interpretability and Trustworthiness
15.6.6 Summary
15.7 Future Directions and Emerging Trends
15.7.1 Hybrid ANN-SNN Architectures
15.7.2 Neuromorphic Edge Devices for Decentralized Healthcare
15.7.3 Self-Adaptive and Continual Learning Systems
15.7.4 Event-Based Sensors and Neuromorphic Imaging Pipelines
15.7.5 Neuro-Symbolic and Explainable AI Models
15.7.6 Integration with Multimodal and Multisensor Systems
15.7.7 A Vision for the Future
15.8 Conclusion
References
16. AI in Personalized Medicine Using Neuromorphic Systems
Abhishek Choubey and Shruti Bhargava Choubey
16.1 Introduction to Personalized Medicine
16.2 Overview of Neuromorphic Computing
16.3 The Role of AI in Healthcare
16.4 Integration of AI and Neuromorphic Systems
16.5 Data Acquisition in Personalized Medicine
16.5.1 Types of Medical Data
16.5.2 Data Privacy and Ethics
16.6 Machine Learning Techniques
16.6.1 Supervised Learning
16.6.2 Unsupervised Learning
16.6.3 Reinforcement Learning
16.7 Neuromorphic Hardware Architectures
16.7.1 SpiNNaker Systems
16.7.2 TrueNorth Chip
16.8 Applications of AI in Personalized Medicine
16.8.1 Genomic Medicine
16.8.2 Patient Monitoring Systems
16.9 Challenges in Implementing Neuromorphic Systems
16.9.1 Technical Limitations
16.9.2 Scalability Issues
16.10 Future Trends in AI and Neuromorphic Systems
16.10.1 Potential Innovations
16.10.2 Ethical Considerations
16.11 Case Studies in Personalized Medicine
16.11.1 Implementations
16.12 Collaboration Between AI and Medical Professionals
16.13 Regulatory Framework for AI in Healthcare
16.14 Public Perception of AI in Medicine
16.15 Conclusion
References
17. An Improved Ensemble Classifier Model to Combat the Escalating Threat of Viral Infections
Sirisha Potluri, Shalini Ramanathan, Vikas B., Puranam Revanth Kumar, K. Manasa Mrunaalini and K. Dheeptha
17.1 Introduction
17.2 Related Work
17.3 Proposed Ensemble Classification Model for Dengue Analysis and Prediction
17.3.1 Flowchart
17.3.2 Algorithm
17.4 Results Analysis and Discussion
17.5 Future Perspectives
References
18. Advanced Ensemble-Based Machine Learning Framework for Brain-Inspired AI and Neuromorphic Computing
Pinakshi Panda, Sukant Kishoro Bisoy and Soumya Sahoo
18.1 Introduction
18.2 Related Work
18.3 Materials and Methods
18.3.1 Maximum Relevance and Minimum Redundancy (MRMR)
18.3.2 Salp Swarm Optimization Algorithm (SSA)
18.3.3 Support Vector Machine (SVM)
18.3.4 Naive Bayesian (NB)
18.3.5 Brain-Inspired AI and Neuromorphic Computing
18.4 Proposed Work
18.5 Performance Assessment
18.6 Conclusion
References
19. DeepSpiker-DiaNet: A Neuromorphic Framework for Interpretable and Energy-Efficient Disease Prediction
Diviya Prabha V. and Rathipriya R.
19.1 Introduction
19.2 Challenges in Deploying AI for Healthcare
19.3 Literature Survey
19.4 Proposed Work
19.5 Experimental Result
19.6 Conclusion
Bibliography
20. Neuromorphic Innovations in Medical Imaging and Diagnostics: Bridging Biological Intelligence and Computational Advances
Kolla Bhanu Prakash and V. Pradeep Kumar
Introduction
Biological Inspiration: The Brain as a Model
Neuro-Inspired Computational Techniques
Advances in Medical Imaging
Diagnosis and Disease Detection
Case Studies
Retinal Imaging with Neuro-Ophthalmic Vision Systems
Prediction of Epileptic Seizures with SNNs
Neuro-Inspired Chips for Low-Power Tumor Detection at the Edge
Advantages and Limitations
Future Trends and Research Opportunities
Conclusion
Bibliography
21. The Future of Neuromorphic Computing in Health Care
Ajay Sharma and Arun Malik
21.1 Introduction
21.2 Historical Context of Neuromorphic Computing
21.3 Current State of Neuromorphic Computing
21.4 Applications in Health Care
21.5 Conclusion and Future Scope
References
Index
Back to Top
Preface
Acknowledgement
Part I: Foundations and Emerging Paradigms in Neuromorphic Healthcare
1. Neuromorphic Computing and Multidisciplinary Healthcare
Teams
Archana Chhabra, Harpreet Kaur and Deepika Ghai
1.1 Overview of Healthcare Neuromorphic Computing
1.1.1 Definition and Main Principles of Neuromorphic Computing
1.1.2 Neuromorphic Computing’s Evolution
1.1.3 Importance of Neuromorphic Computing in Healthcare
1.2 Fundamental Ideas in Neuromorphic Computing for Medical Applications
1.2.1 Neuromorphic Hardware and Architecture
1.2.1.1 Key Hardware Platforms
1.2.2 Spiking Neural Networks (SNNs) in Healthcare
1.2.3 Energy-Efficient AI for Medical Applications
1.2.3.1 Low-Power Computing for Wearable Devices
1.2.4 Role of Neuromorphic Computing in Edge AI for Healthcare
1.3 Applications of Neuromorphic Computing in Healthcare
1.3.1 Medical Imaging and Diagnostics
1.3.2 Wearable Consumer Electronics for Health Monitoring
1.3.3 Personalized Medicine and Treatment Optimization
1.4 Role of Multidisciplinary Healthcare Teams
1.4.1 Composition of Multidisciplinary Teams
1.4.2 Collaboration Challenges and Solutions in AI-Driven
Healthcare
1.4.2.1 Challenges
1.4.2.2 Solutions
1.4.3 A Case Study of a Successful Interdisciplinary Healthcare Project
1.5 Conclusion, Future Perspectives, and Research Challenges
1.5.1 Future Perspectives
1.5.1.1 Advancements in Neuromorphic Hardware for Real-Time Healthcare Applications
1.5.1.2 Integration with Edge AI, IoT, and Cloud-Based Healthcare Systems
1.5.1.3 Potential Impact on Remote Patient Monitoring and Telemedicine
1.5.2 Research Challenges in Clinical Implementation and Scalability
References
2. Neuromorphic AI in Medical Diagnostics
S. Mohanraj and R. Sujatha
2.1 Introduction
2.1.1 Overview of AI in Medical Diagnostics
2.1.2 Limitations of Traditional Deep Learning Models in Medical Diagnostics
2.1.3 Introduction to Neuromorphic AI and Its Significance in Healthcare
2.2 Fundamentals of Neuromorphic AI
2.2.1 Definition and Principles
2.2.2 Spiking Neural Networks (SNNs) and Event-Based Processing
2.2.3 Comparison with Conventional Deep Learning Methods
2.2.4 Advantages of Neuromorphic Computing
2.3 Applications in Medical Diagnostics
2.3.1 Cancer Screening
2.3.2 Cardiovascular Risk Stratification
2.3.3 Diagnosis of Neurological Diseases
2.3.4 Personalized Medicine
2.4 Technological Innovations and Hardware Implementations
2.4.1 Interpretability and Data Privacy
2.4.2 Neuromorphic Hardware Innovations
2.4.3 Comparing Neuromorphic AI With Traditional Deep Learning
2.5 Interpretability, Ethics, and Regulatory Considerations
2.5.1 Interpretability Challenges in Neuromorphic AI
2.5.2 Data Privacy and Compliance
2.5.3 Regulatory Challenges and Future Directions
2.6 Comparative Analysis: Neuromorphic AI vs. Traditional Deep Learning
2.7 Conclusion and Future Prospects
2.7.1 Summary of Key Findings
2.7.2 Potential of Neuromorphic AI in Next-Gen Medicine
2.7.3 Future Research Directions and Emerging Trends
Conclusion
References
3. Addressing Scalability and Reliability in Neuromorphic
Healthcare
Manas Kumar Yogi, K. V. V. Subba Rao, Shanmukha Mahathi Chinta and S. Soha Tabassum
3.1 Introduction to Neuromorphic Healthcare
3.1.1 Overview of Neuromorphic Systems in Healthcare
3.1.2 The Role of Scalability and Reliability in Neuromorphic Healthcare
3.1.3 A New Standard for Intelligent Healthcare
3.2 Overcoming Scalability and Reliability Challenges in Neuromorphic Healthcare
3.2.1 Scalability Challenges in Neuromorphic Healthcare
3.2.2 Reliability Challenges in Neuromorphic Healthcare
3.2.3 Balancing Performance, Efficiency, and Accuracy
3.2.4 Design Guidelines for Robust Models for Scalable and Reliable Neuromorphic Systems
3.3 Construction of Proposed Model
3.3.1 Implementation of the SBFL Model
3.3.2 Training, Optimization, and Algorithm Details
3.3.3 Results and Performance Evaluation
3.3.4 Results and Discussion
3.3.5 Statistical Validation
3.3.6 Case Study: Real-World Deployment in a Hospital Network
3.4 System Optimization and Performance Enhancements
3.4.1 Cutting-Edge Optimization Techniques
3.4.2 Precision Performance Tuning
3.4.3 Real-World Adaptability and Resilience
3.4.4 Futuristic Enhancements and Next Steps
3.5 Future Enhancements of the SBFL Model
3.5.1 Adaptive Thresholding for Dynamic Learning
3.5.2 Neuromorphic Hardware Accelerators
3.5.3 Bio-Plausible Learning Rules
3.5.4 Hierarchical SNN Architectures
3.5.5 Hybrid Quantum-SNN Models
3.5.6 Self-Adaptive and Multi-Agent SBFL
3.5.7 Lifelong Learning and Holographic Memory
3.6 Conclusion
3.6.1 Significant Takeaways
3.6.2 Closing Reflections
References
4. A Comprehensive Review of Brain-Computer Interfaces:
Current Research Trends in Person-Centric Healthcare and Ambient Assisted Living
P. Nagaraj, Josephine Selle Jeyanathan, A. Lakshmi, V. Muneeswaran and K. Muthamil Sudar
4.1 Introduction to Brain-Computer Interface (BCI)
4.1.1 Definition and Overview
4.1.2 Significance of BCI Technology
4.1.3 Schematic Architecture of BCI
4.1.4 Benefits of BCI
4.2 Principles of Brain Signal Acquisition
4.2.1 Electroencephalography
4.2.2 Invasive Methods for Brain Signal Acquisition
4.3 Feature Extraction Methods in BCI
4.3.1 Time-Domain Features
4.3.2 Frequency-Domain Features
4.3.3 Time-Frequency Domain Features
4.4 BCI Modalities and Technologies
4.4.1 Non-Invasive BCI Technology
4.4.1.1 Non–Invasive EEG Electrode Materials
4.4.1.2 Durability
4.4.1.3 The Quality of Biocompatibility
4.4.2 Invasive BCI Technology
4.4.2.1 Classification of Invasive BCI
4.5 Applications of BCI
4.5.1 Healthcare Applications
4.5.2 Rehabilitation Applications
4.5.3 Communication and Assistive Technology
4.6 Challenges and Future Directions
4.6.1 Signal Quality
4.6.2 Adaptability and Cost Effectiveness
4.6.3 Safety and Ethical Factors
4.6.4 Future Directions
References
5. Neuromorphic Systems for BCI and Neuroprosthetics
T.S. Suganya and Midhun Ramesh
5.1 Introduction
5.2 What are Neuromorphic Systems?
5.3 How Brain-Computer Interfaces (BCIs) Work
5.4 What are Neuroprosthetics?
5.5 Neuromorphic Hardware for BCIs and Neuroprosthetics
5.6 Signal Acquisition and Processing in BCI Systems
5.7 Applications of Neuromorphic Systems in BCI and Neuroprosthetics
5.8 Challenges and Future Directions
5.9 Types of Neuromorphic Architectures and Their Role in BCIs and Neuroprosthetics
5.10 Comparative Study: Neuromorphic vs Traditional Systems in BCIs
5.11 Case Studies and Real-World Implementations
5.12 Ethical, Legal, and Societal Implications of Neuromorphic
BCIs
5.13 Interdisciplinary Research and Future Scope
5.14 Educational and Skill Development Aspects
5.15 Integration with Other Emerging Technologies
5.16 User Experience and Accessibility in Neuromorphic BCIs
5.17 Safety and Reliability in Neuromorphic Systems
5.18 Economic Impact and Market Trends
5.19 Regulatory and Standardization Challenges for Neuromorphic BCIs
Conclusion
Bibliography
6. Neuromorphic Computing for Wearable Health Devices
Wasswa Shafik
6.1 Introduction
6.1.1 Definition and Principles
6.1.2 Historical Development
6.1.3 Application Overview in Health Devices
6.2 Wearable Health Devices
6.2.1 Types and Features
6.2.2 Importance of Neuromorphic Computing in Wearable Health Devices
6.2.2.1 Enhanced Processing Efficiency
6.2.2.2 Low Power Consumption
6.3 Challenges and Limitations of Current Computing Technologies
6.3.1 Processing Speed
6.3.2 Energy Efficiency
6.4 Neuromorphic Computing Architectures
6.4.1 Spiking Neural Networks
6.4.2 Memristor-Based Architectures
6.5 Machine Learning Algorithms for Health Monitoring
6.5.1 Supervised and Unsupervised Learning
6.5.2 Deep Learning Models
6.6 Applications of Neuromorphic Computing in Wearable Health Devices
6.6.1 Real-Time Health Monitoring
6.6.2 Disease Detection and Diagnosis
6.7 Future Directions and Research Opportunities
6.7.1 Integration with Internet of Things (IoT)
6.7.2 Biocompatible Materials for Wearables
6.7.3 Advancements in Neuromorphic Hardware
6.7.4 Personalized Healthcare
6.8 Conclusion
References
7. Neuro-Inspired Advances in Imaging and Diagnostics
Ajay Sharma and Arun Malik
7.1 Introduction
7.1.1 Neuroscience Principles in Imaging
7.2 Bio-Inspired Computational Models
7.3 Neural Mechanisms in Vision and Imaging
7.4 Advancements in Imaging Techniques
7.5 Neuro-Inspired Diagnostics
7.5.1 Applications in Cancer Detection, Cardiology, and Ophthalmology
7.6 Clinical Applications and Challenges of Neuro‑Inspired
Imaging in Healthcare
7.7 Ethical, Regulatory, and Practical Challenges in Implementation
Conclusion
References
8. Harnessing Brain-Inspired AI for Healthcare’s Digital Future
Bittoo Kumar Sharma
8.1 Overview of AI in Healthcare Digital Transformation
8.1.1 The Role of AI in Digital Healthcare Transformation
8.1.2 AI in Healthcare Operations and Management
8.1.3 AI in Remote and Virtual Healthcare
8.1.4 Challenges and Ethical Considerations in AI-Driven Healthcare
8.1.4.1 Data Privacy and Security Concerns
8.1.4.2 Bias in AI Algorithms
8.1.4.3 The Role of Human Oversight
8.1.5 Future Prospects of AI in Healthcare Digital Transformation
8.2 Limitations of Traditional AI Systems in Healthcare
8.2.1 The Challenges of Traditional AI in Healthcare
8.2.2 Ethical and Regulatory Challenges
8.3 The Paradigm Shift towards Brain-Inspired AI Introduction
8.3.1 How Brain-Inspired AI Works
8.3.1.1 Neuromorphic Computing: Mimicking the Human Brain
8.3.1.2 Cognitive AI: Learning and Adaptability
8.3.2 Applications of Brain-Inspired AI in Healthcare
8.3.2.1 AI for Personalized Diagnostics and Treatment
8.3.2.2 Real-Time Disease Monitoring
8.3.2.3 AI-Powered Robotics in Surgery
8.3.3 Understanding Brain-Inspired AI and Neuromorphic Computing
8.4 Conceptual Foundation of Brain-Inspired AI
8.4.1 Understanding Brain-Inspired AI?
8.4.2 Key Characteristics of Brain-Inspired AI:
8.4.3 Differences between Brain Inspired AI and Traditional AI
8.4.4 Real-World Applications of Brain-Inspired AI
8.4.4.1 Key Applications of Brain-Inspired AI
8.4.4.2 AI in Healthcare: Smarter Diagnosis and Personalized Treatment
8.5 Neuromorphic Computing: Bridging AI and Neuroscience
8.5.1 What is Neuromorphic Computing?
8.5.1.1 Key Features of Neuromorphic Computing
8.5.2 How Neuromorphic Computing Connects AI and Neuroscience
8.5.2.1 Mimicking Biological Neurons and Synapses
8.5.2.2 Real-Time Learning and Adaptability
8.5.2.3 Future Application of Neuromorphic Computing
8.5.3 Core Differences between Traditional AI and Brain-Inspired AI
8.5.4 Biological Neural Networks vs. Artificial Neural Networks
8.6 Uses of AI Inspired by the Brain in Healthcare
8.6.1 Diagnostics and Imaging in Medicine
8.6.2 Early Disease Detection and Predictive Analytics
8.6.3 Individualized Medicine and Therapy Programs
8.6.4 Intelligent Automation and Robotic Surgery
8.6.5 Wearable Technology and Remote Patient Monitoring
8.6.6 Diagnosing Neurological Disorders and Cognitive Health
8.7 Benefits of AI Inspired by the Brain for Digital Healthcare Systems
8.7.1 Making Decisions in Real Time
8.7.2 Efficiency and Low Energy Use
8.7.3 Minimal Data Adaptive Learning
8.7.4 Improved Transparency and Interpretability
8.7.5 Scalability in Healthcare Ecosystems
8.8 Challenges and Ethical Considerations in Brain-Inspired AI for Healthcare
8.8.1 Security and Privacy of Data
8.8.1.1 Neuromorphic AI in Healthcare: Ethical Frameworks
8.8.1.2 Complexity of Computation and Hardware Restrictions
8.8.1.3 Describe Aptitude and Bias in Brain-Inspired Models
8.8.2 Adherence to Regulations in Brain Medical Applications
8.8.3 AI Inspired by the Brain in Medical Imaging Systems
8.8.4 Wearable Medical Technology with Neuromorphic Sensors
8.8.5 Healthcare Predictive Systems for the Management of Chronic Illnesses
8.8.6 Brain-Machine Interfaces for the Treatment of Paralysis
8.9 Future Directions of Brain-Inspired AI in Healthcare
8.9.1 Collaboration with IoT and Quantum Computing
8.9.2 Intelligible and Reliable AI Models
8.9.3 Developments in Neuromorphic Hardware
8.9.4 AI-Powered Genomics and Drug Discovery
8.9.5 The Ethical and Policy Framework for Next-Gen AI
Conclusion
Recommendations
Bibliography
Part II: Ethical, Behavioral, and Application-Specific Innovations
9. Ethical and Legal Challenges in Using Neuromorphic AI for Diagnosing and Treating Childhood Trauma
Navonita Mallick, Shashwata Sahu and Hiranmaya Nanda
9.1 Introduction
9.2 Research Objectives and Methodology
9.3 Neuromorphic AI in Healthcare: A Technical Overview
9.4 Understanding Childhood Tauma Clinical and Ethical Context
9.5 Ethical Challenges in Using Neuromorphic AI for Diagnosing and Treating Childhood Trauma
9.6 Legal Challenges: Accountability, Data Protection, and Global Frameworks
9.7 Comparative Analysis: Global Best Practices and Policy Gaps in Regulating Neuromorphic AI in Healthcare
9.8 Proposed Framework for Ethical and Legal Governance of Neuromorphic AI in Pediatric Mental Health
9.9 Conclusion
References
10. Neuromorphic Computing in Sports: New Age Technology in Transforming Athlete Performance and Rehabilitation
Amitava Pal, Rajkumar M. and Kavita Mathad
Introduction
Objective of the Chapter
Neuromorphic Computing: Concepts and Capabilities
Practical Scenario: Neuromorphic Computing in Action for Athletes
Current Applications in Sports
Game Strategy and Decision-Making Support
Integration with Sports Technology Ecosystem
Future Innovations and Possibilities: Expanded Use Cases and Scenarios
Challenges and Limitations
Implications for Sports Science and Healthcare
Discussion Points
Conclusion
Declaration
Abbreviations
References
11. An Intelligent System for Early Mental Health Detection: Integrating Wearables, Social Media, and Neuromorphic
Richa Mathur and Rakesh Roshan
11.1 Introduction
11.2 Literature Review
11.2.1 Comparison of Results from Previous Studies
11.3 Problem Definition
11.4 Methodology
11.4.1 Dataset Description
11.4.1.1 Data Collection Sources
11.4.1.2 Dataset Entries
11.4.2 Data Preprocessing
11.4.3 Platforms and Tools
11.4.4 Algorithms Used
11.5 Proposed Model for Early Detection of Mental Health Issues
11.5.1 Flow Diagram (DFD)
11.5.2 Proposed Workflow Pipeline
11.6 Experimental Results: Accuracy Table
11.7 Ethical Considerations
11.8 Conclusion
References
12. Neuromorphic AI-Driven Personalization and Predictive
Analytics in Digital Health: A Marketing Perspective on Patient Engagement Metrics and Outcomes
Rahul Chauhan and Bhoomi Chauhan
Introduction
Literature Review
Research Methodology
Objectives
Analysis
Conclusion
Acknowledgement Statement
References
13. Building Neuromorphic Systems for Medicine
Devanshi Srivastava and Adarsh Kumar Arya
13.1 Introduction
13.2 Fundamentals of Neuromorphic Computing
13.2.1 Key Principles of Brain-Inspired Computing
13.2.2 Comparison of Neuromorphic Systems for Medicine with Traditional Computing Architectures
13.2.3 Advantages of Neuromorphic Systems for Medical Applications
13.3 Neuromorphic Hardware Platforms
13.3.1 Overview of Existing Neuromorphic Chips and Systems
13.3.2 Specific Hardware Considerations in Neuromorphic Systems
13.3.3 Challenges and Opportunities of Neuromorphic Systems
13.4 Neuromorphic Algorithms for Medical Data Processing
13.4.1 Spiking Neural Networks (SNNs) and Their Applications in Medicine
13.4.2 Learning and Adaptation in Neuromorphic Systems
13.4.3 Real-Time Processing of Complex Medical Data in Neuromorphic Systems
13.5 Applications in Medical Imaging in Neuromorphic Systems
13.5.1 Neuromorphic Approaches to Image Reconstruction and Enhancement
13.5.2 Fast and Efficient Analysis of Medical Images in Neuromorphic Systems
13.5.3 Integration with Existing Medical Imaging Technologies in Neuromorphic Systems
13.6 Neuromorphic Systems for Bio Signal Processing
13.6.1 EEG, ECG, and EMG Signal Analysis Using Neuromorphic Architectures
13.6.2 Real-Time Monitoring and Anomaly Detection
13.7 Neuromorphic Approaches in Drug Discovery and Development
13.7.1 Accelerating Drug Screening Processes in Neuromorphic Systems for Medicine
13.7.2 Predicting Drug Interactions and Side Effects in Neuromorphic Systems for Medicine
13.8 Personalized Medicine and Treatment Planning through Neuromorphic Systems
13.8.1 Adaptive Learning for Patient-Specific Models in Neuromorphic Systems
13.8.2 Real-Time Treatment Optimization in Neuromorphic Systems for Medicine
13.8.3 Integration of Neuromorphic Systems with Electronic Health Records
13.9 Future Directions and Emerging Trends of Neuromorphic Systems for Medicine
13.9.1 Integrating Neuromorphic Systems with Other Emerging Technologies
13.9.2 Potential Breakthroughs in Neurodegenerative Disease Research
13.9.3 Scaling Neuromorphic Systems for Large-Scale Medical Applications
13.10 Conclusion
References
14. Intelligent Neuromorphic Frameworks for Neural Interfaces and Prosthetic Control
Aditya Dayal Tyagi, Himani Tyagi, Setu Garg and Vivek Tomar
14.1 Introduction
14.1.1 Understanding Brain-Computer Interfaces and Neuroprosthetics
14.1.2 The Role of AI in Interpreting Brain Signals
14.1.3 Emergence of Neuromorphic Systems as Brain-Inspired Processors
14.1.4 Relevance to Healthcare and Assistive Technologies
14.2 Biological Inspiration behind Neuromorphic Computing
14.2.1 Fundamentals of Neural Coding and Brain Signal Processing
14.2.2 Spiking Neural Networks (SNNs) and Brain-Like Computation
14.2.3 Comparison with Traditional von Neumann Architectures
14.2.4 Need for Real-Time, Adaptive Signal Processing in BCIs and Neuroprosthetics
14.3 Architecture of Neuromorphic Systems
14.3.1 Overview of Neuromorphic Hardware: IBM TrueNorth, Intel Loihi, SpiNNaker, BrainScaleS
14.3.2 Event-Driven Architecture and Asynchronous Processing
14.3.3 Integration with Bio Signals and Sensors (EEG, EMG, ECoG)
14.4 Neuromorphic Interfaces for Brain-Computer Interaction
14.4.1 Signal Acquisition, Preprocessing, and Encoding into Spike Trains
14.4.2 Real-Time Decoding Using Spiking Neural Networks (SNNs)
14.4.3 Closed-Loop BCI Using Neuromorphic Systems
14.4.4 Case Studies: Motor Imagery Decoding, Cognitive Load Estimation, and More
14.5 Applications in Neuroprosthetics
14.5.1 Prosthetic Limb Control Using BCI and Neuromorphic Processors
14.5.2 Cochlear and Retinal Implants
14.5.3 Adaptive Learning in Neuromorphic Prosthetics
14.5.4 Energy Efficiency and Miniaturization Benefits
14.6 Co-Design of Neuromorphic Hardware and Algorithms
14.6.1 Bio-Signal Friendly Neural Encoding and Learning Rules
14.6.2 On-Chip Learning Mechanisms
14.6.3 Brain-Inspired Adaptation and Plasticity for Personalized Care
14.7 Challenges and Limitations
14.7.1 Signal Variability and Noise in Neural Data
14.7.2 Scalability and Interface Complexity
14.7.3 Ethical and Regulatory Concerns
14.7.4 Integration with Wearable or Implantable Devices
14.8 Future Prospects
14.8.1 Neuromorphic Chips in Embedded and Edge BCIs
14.8.2 Bi-Directional BCIs for Sensory Feedback
14.8.3 Federated Neuromorphic Learning for Multi-User Healthcare Systems
14.8.4 Synergies with Brain Organoids and Digital Twins
14.9 Conclusion
References
Part III: Advanced Models, Diagnostics, and Future Outlook
15. Neuromorphic Innovations in Medical Diagnosis and Imaging
Himani Tyagi, Aditya Dayal Tyagi, Kimmi Verma and Ravi Prakash Chaturvedi
15.1 Introduction
15.2 Foundation of Neuro-Inspired Computing
15.2.1 What is Brain-Inspired Computing
15.2.2 Spiking Neural Networks (SNNs): Core Building Block
15.2.3 Neuromorphic Sensors and Data Acquisition
15.2.4 Neuromorphic Hardware Platforms
15.2.5 Comparison with Conventional AI
15.3 Diagnostic Imaging: Current Landscape
15.3.1 Role of AI in Medical Imaging
15.3.2 Challenges in Conventional AI-Based Imaging
15.3.3 Need for Real-Time, Low-Power Diagnostics
15.3.4 Early Signs of Neuromorphic Adoption
15.4 Neuro-Inspired Techniques in Imaging
15.4.1 Spiking-Based Image Segmentation
15.4.2 Denoising and Image Enhancement
15.4.3 Spiking Attention Models and Transformers
15.4.4 Real-Time, Low-Power Inference
15.4.5 Emerging Use Cases in Clinical Practice
15.5 Comparative Evaluation and Key Insights
15.5.1 Comparative Assessment and Key Findings
15.5.2 Power Consumption and Deployability at the Edge
15.5.3 Real-Time Inference and Latency
15.5.4 Interpretability and Biological Plausibility
15.5.5 Table of Main Takeaways
15.6 Challenges and Limitations
15.6.1 Training Complexity and Optimization Constraints
15.6.2 Hardware Access and Ecosystem Fragmentation
15.6.3 Dataset Incompatibility and Lack of Temporal Benchmarks
15.6.4 Regulatory Complexity and Clinical Novelty
15.6.5 Interpretability and Trustworthiness
15.6.6 Summary
15.7 Future Directions and Emerging Trends
15.7.1 Hybrid ANN-SNN Architectures
15.7.2 Neuromorphic Edge Devices for Decentralized Healthcare
15.7.3 Self-Adaptive and Continual Learning Systems
15.7.4 Event-Based Sensors and Neuromorphic Imaging Pipelines
15.7.5 Neuro-Symbolic and Explainable AI Models
15.7.6 Integration with Multimodal and Multisensor Systems
15.7.7 A Vision for the Future
15.8 Conclusion
References
16. AI in Personalized Medicine Using Neuromorphic Systems
Abhishek Choubey and Shruti Bhargava Choubey
16.1 Introduction to Personalized Medicine
16.2 Overview of Neuromorphic Computing
16.3 The Role of AI in Healthcare
16.4 Integration of AI and Neuromorphic Systems
16.5 Data Acquisition in Personalized Medicine
16.5.1 Types of Medical Data
16.5.2 Data Privacy and Ethics
16.6 Machine Learning Techniques
16.6.1 Supervised Learning
16.6.2 Unsupervised Learning
16.6.3 Reinforcement Learning
16.7 Neuromorphic Hardware Architectures
16.7.1 SpiNNaker Systems
16.7.2 TrueNorth Chip
16.8 Applications of AI in Personalized Medicine
16.8.1 Genomic Medicine
16.8.2 Patient Monitoring Systems
16.9 Challenges in Implementing Neuromorphic Systems
16.9.1 Technical Limitations
16.9.2 Scalability Issues
16.10 Future Trends in AI and Neuromorphic Systems
16.10.1 Potential Innovations
16.10.2 Ethical Considerations
16.11 Case Studies in Personalized Medicine
16.11.1 Implementations
16.12 Collaboration Between AI and Medical Professionals
16.13 Regulatory Framework for AI in Healthcare
16.14 Public Perception of AI in Medicine
16.15 Conclusion
References
17. An Improved Ensemble Classifier Model to Combat the Escalating Threat of Viral Infections
Sirisha Potluri, Shalini Ramanathan, Vikas B., Puranam Revanth Kumar, K. Manasa Mrunaalini and K. Dheeptha
17.1 Introduction
17.2 Related Work
17.3 Proposed Ensemble Classification Model for Dengue Analysis and Prediction
17.3.1 Flowchart
17.3.2 Algorithm
17.4 Results Analysis and Discussion
17.5 Future Perspectives
References
18. Advanced Ensemble-Based Machine Learning Framework for Brain-Inspired AI and Neuromorphic Computing
Pinakshi Panda, Sukant Kishoro Bisoy and Soumya Sahoo
18.1 Introduction
18.2 Related Work
18.3 Materials and Methods
18.3.1 Maximum Relevance and Minimum Redundancy (MRMR)
18.3.2 Salp Swarm Optimization Algorithm (SSA)
18.3.3 Support Vector Machine (SVM)
18.3.4 Naive Bayesian (NB)
18.3.5 Brain-Inspired AI and Neuromorphic Computing
18.4 Proposed Work
18.5 Performance Assessment
18.6 Conclusion
References
19. DeepSpiker-DiaNet: A Neuromorphic Framework for Interpretable and Energy-Efficient Disease Prediction
Diviya Prabha V. and Rathipriya R.
19.1 Introduction
19.2 Challenges in Deploying AI for Healthcare
19.3 Literature Survey
19.4 Proposed Work
19.5 Experimental Result
19.6 Conclusion
Bibliography
20. Neuromorphic Innovations in Medical Imaging and Diagnostics: Bridging Biological Intelligence and Computational Advances
Kolla Bhanu Prakash and V. Pradeep Kumar
Introduction
Biological Inspiration: The Brain as a Model
Neuro-Inspired Computational Techniques
Advances in Medical Imaging
Diagnosis and Disease Detection
Case Studies
Retinal Imaging with Neuro-Ophthalmic Vision Systems
Prediction of Epileptic Seizures with SNNs
Neuro-Inspired Chips for Low-Power Tumor Detection at the Edge
Advantages and Limitations
Future Trends and Research Opportunities
Conclusion
Bibliography
21. The Future of Neuromorphic Computing in Health Care
Ajay Sharma and Arun Malik
21.1 Introduction
21.2 Historical Context of Neuromorphic Computing
21.3 Current State of Neuromorphic Computing
21.4 Applications in Health Care
21.5 Conclusion and Future Scope
References
Index
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