Preface xxiii Abhishek KUMAR, Priya BATTA and J.P. ANANTH Chapter 1. Artificial Intelligence in Healthcare: Principles, Paradigms and Emerging Trends 1 Shilpa C. PATIL and Salim Allauddin CHAVAN 1.1. Introduction 2 1.2.

Principles of AI in healthcare 3 1.3. Paradigms of AI in healthcare 5 1.4. Emerging trends in AI-driven healthcare 7 1.5. Challenges and limitations 10 1.6.

Future directions 12 1.7. Conclusion 12 1.8. References 13 Chapter 2. Machine Learning Models for Diagnostic Decision-Making in Neurology 17 Sunil Ramrao YADAV and Kalpana MALPE 2.1. Introduction 17 2.

2. Overview of ML in healthcare 19 2.3. Supervised learning models in neurological diagnosis 20 2.4. Unsupervised and semi-supervised approaches 21 2.5. DL for neuroimaging and signal analysis 24 2.

6. Multimodal and integrative diagnostic models 27 2.7. XAI and clinical interpretability 28 2.8. Future directions in ML for neurological diagnostics 30 2.9. Conclusion 31 2.

10. References 31 Chapter 3. Deep Learning Approaches to Neuroimaging and Brain Mapping 35 G.V. RAMDAS and G.M. VAIDYA 3.1.

Introduction 35 3.2. Deep learning fundamentals for neuroimaging 37 3.3. Applications in structural neuroimaging (MRI, CT) 41 3.4. Applications in functional neuroimaging (fMRI, PET, EEG/MEG) 43 3.5.

Brain mapping and connectomics with deep learning 45 3.6. Clinical applications and translational potential 46 3.7. Challenges, limitations and future directions 48 3.8. Conclusion 50 3.9.

References 50 Chapter 4. Predictive Analytics for Early Detection of Neurodegenerative Disorders 53 Debabrata SAHANA and K. GAVHALE 4.1. Introduction 53 4.2. Predictive analytics framework for neurodegenerative disorders 55 4.3.

Applications of predictive analytics in specific neurodegenerative disorders 59 4.4. Emerging trends and methodological advances 62 4.5. Challenges, ethical considerations and future directions 64 4.6. Conclusion 67 4.7.

References 67 Chapter 5. AI-Enhanced Stroke Diagnosis, Prognosis and Rehabilitation Pathways 71 Rahul PATIL and Fazil SHEIKH 5.1. Introduction 71 5.2. AI in stroke diagnosis 73 5.3. AI in stroke prognosis 75 5.

4. AI in stroke rehabilitation pathways 77 5.5. Integration into clinical workflows 79 5.6. Future directions 81 5.7. Conclusion 83 5.

8. References 84 Chapter 6. Computational Biomarker Discovery for Neurological and Psychiatric Disorders 87 Chaitnya GODBOLE and Shamla MANTRI 6.1. Introduction 87 6.2. Computational approaches for biomarker discovery 89 6.3.

Machine learning and AI in biomarker identification 92 6.4. Biomarkers in neurological disorders 96 6.5. Biomarkers in psychiatric disorders 98 6.6. Challenges and future directions 100 6.7.

Conclusion 102 6.8. References 103 Chapter 7. Natural Language Processing for Clinical Narratives and Neurological Case Records 107 Shrikrishna N. BAMNE and Swapna KAMBLE 7.1. Introduction 108 7.2.

NLP fundamentals in clinical narratives 109 7.3. Applications in neurology and case records 111 7.4. Advances in model architectures 113 7.5. Clinical Utility: diagnosis, prognosis and treatment support 115 7.6.

Integration with EHR and clinical workflows 117 7.7. Challenges: bias, privacy, data scarcity and interpretability 118 7.8. Future perspectives 120 7.9. Conclusion 121 7.10.

References 122 Chapter 8. AI-Integrated Wearable Technologies for Continuous Neurological Monitoring 125 Swati JAGTAP and Ashish N. PATIL 8.1. Introduction 125 8.2. AI in wearable neurological monitoring 127 8.3.

Clinical applications 129 8.4. System architecture and data integration 133 8.5. Challenges and limitations 136 8.6. Future directions 138 8.7.

Conclusion 139 8.8. References 140 Chapter 9. Epilepsy Forecasting and Seizure Prediction Through AI Algorithms 143 Ashwini R. GARGATE and Komal M. JUJAR 9.1. Introduction 144 9.

2. Pathophysiology and challenges of seizure prediction 145 9.3. AI in epilepsy forecasting: an overview 146 9.4. Machine learning approaches for seizure prediction 148 9.5. Deep learning and neural network models 150 9.

6. Multimodal data integration for seizure forecasting 152 9.7. Wearable devices and real-time forecasting 154 9.8. Privacy, ethics and data challenges 155 9.9. Future directions in AI-driven seizure forecasting 156 9.

10. Conclusion 157 9.11. References 158 Chapter 10. Intelligent Robotic Systems for Neurorehabilitation and Assistive Care 161 Debabrata SAHANA and Atul Namdev PAWAR 10.1. Introduction 162 10.2.

Principles of intelligent robotic systems 162 10.3. Robotics in neurorehabilitation 164 10.4. Assistive robotics for daily living 166 10.5. Technological paradigms and enablers 167 10.6.

Clinical evidence and applications 168 10.7. Challenges and limitations 171 10.8. Emerging trends and future directions 173 10.9. Conclusion 174 10.10.

References 175 Chapter 11. Personalized Medicine in Multiple Sclerosis Through AI-Driven Analytics 179 Chaitnya GODBOLE and Shrikant Rangrao KADAM 11.1. Introduction 180 11.2. Overview of multiple sclerosis and the need for personalization 181 11.3. AI in MS diagnosis and early detection 181 11.

4. AI-driven prognostic modeling in MS 183 11.5. Personalized treatment strategies through AI analytics 184 11.6. Integration of multi-omics and biomarkers 186 11.7. Role of neuroimaging and computer vision 187 11.

8. AI-powered monitoring and patient engagement 188 11.9. Challenges, ethical concerns and limitations 190 11.10. Future directions and clinical translation 191 11.11. Conclusion 193 11.

12. References 193 Chapter 12. Artificial Intelligence Applications in Sleep Medicine and Neurological Disorders 197 Swati JAGTAP and Sharifnawaj Y. INAMDAR 12.1. Introduction 198 12.2. AI in sleep medicine 199 12.

3. AI in neurological disorders 201 12.4. Multimodal data integration and predictive analytics 203 12.5. Ethical, legal and clinical challenges 206 12.6. Future directions 207 12.

7. Conclusion 208 12.8. References 209 Chapter 13. Virtual and Augmented Reality Coupled with AI for Cognitive Rehabilitation 213 Omkar KULKARNI and Amruta B. KALE 13.1. Introduction 214 13.

2. Foundations of cognitive rehabilitation 215 13.3. VR in cognitive rehabilitation 216 13.4. AR in cognitive rehabilitation 217 13.5. AI for adaptive therapy 218 13.

6. Synergistic role of VR/AR coupled with AI 219 13.7. Clinical applications and case studies 221 13.8. Technological innovations and tools 222 13.9. Challenges and ethical considerations 223 13.

10. Future directions and research opportunities 224 13.11. Conclusion 225 13.12. References 226 Chapter 14. AI-Driven Drug Discovery Pipelines for Neurological and Mental Health Therapies 229 Sharad KSHIRSAGAR and Ashish N. PATIL 14.

1. Introduction 229 14.2. Principles of AI in drug discovery 231 14.3. AI in target identification and biomarker discovery 232 14.4. AI in hit discovery and lead optimization 233 14.

5. AI in drug repurposing for neurological and mental health disorders 235 14.6. AI in preclinical and clinical trial design for neurological and mental health therapies 236 14.7. Ethical, regulatory, and societal implications of AI in neurological and psychiatric drug discovery 238 14.8. Future directions and emerging trends in AI-driven drug discovery for neurological and mental health therapies 241 14.

9. Conclusion 242 14.10. References 242 Chapter 15. Ethical, Legal and Societal Implications of AI in Neurology and Medicine 245 Dipali JANKAR and Anil SAHU 15.1. Introduction 246 15.2.

AI in neurology and medicine: an overview 247 15.3. Ethical implications 249 15.4. Legal implications 251 15.5. Societal implications 253 15.6.

Challenges and future perspectives 256 15.7. Conclusion 258 15.8. References 258 Chapter 16. Federated Learning and Collaborative AI Models in Neuroscience Research 261 Dipali JANKAR and Sanjay L. BADJATE 16.1.

Introduction 261 16.2. Fundamentals of FL in neuroscience 263 16.3. Collaborative AI models in neuroscience 265 16.4. Applications of FL and collaborative AI in neuroscience 267 16.5.

Challenges and limitations of FL and collaborative AI in neuroscience 271 16.6. Future directions 274 16.7. Conclusion 274 16.8. References 275 Chapter 17. AI-enabled Approaches for Pain Prediction, Assessment and Management 279 Mario ANTONY and Salim Allauddin CHAVAN 17.

1. Introduction 279 17.2. AI for pain prediction 281 17.3. AI for pain assessment 283 17.4. AI in pain management 285 17.

5. Challenges and limitations of AI in pain medicine 287 17.6. Ethical, legal and future directions 290 17.7. Conclusion 291 17.8. References 292 Chapter 18.

Conversational AI and Virtual Assistants for Neurological Pat.