{"product_id":"ai-driven-innovations-in-physiotherapy-and-oncology-5-hardback-9781836691334","title":"AI-driven Innovations in Physiotherapy and Oncology 5 (Hardback) 9781836691334","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAI-driven Innovations in Physiotherapy and Oncology 5\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbhishek Kumar (Edited by), Kumar (Author), Priya Batta (Edited by), Sachin Ahuja (Edited by), Pramod Singh Rathore (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781836691334, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 July 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e272 pages\u003cbr\u003e23.5 x 15.6 x 1.8 cm, 0.517 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003eAI-driven Innovations in Physiotherapy and Oncology 5\u003c\/i\u003e explores how artificial intelligence (AI) is transforming modern healthcare by enabling smarter, more precise and patient-centered approaches. This book highlights the application of machine learning, deep learning and data analytics in enhancing rehabilitation and cancer care, from movement analysis and personalized physiotherapy to early detection and precision oncology.\u003c\/p\u003e \u003cp\u003eCombining both theory and practice, this book presents interdisciplinary insights for researchers, clinicians and academicians, while addressing real-world implementations, emerging trends and ethical considerations. The book also positions AI as a key driver in advancing next-generation healthcare systems and improving clinical outcomes.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003cbr\u003e\u003ci\u003eAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHORE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction xix\u003cbr\u003e\u003ci\u003eAbhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHORE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Physiotherapy Patient Records Enhanced with Natural Language Processing 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMandar MALAWADE and Rasika Ranjit CHAFLE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 2\u003cbr\u003e1.2. Physiotherapy patient records: structure, content and challenges 3\u003cbr\u003e1.3. Fundamentals of NLP in healthcare 6\u003cbr\u003e1.4. Applications of NLP in physiotherapy records 9\u003cbr\u003e1.5. Technological frameworks for NLP in physiotherapy 13\u003cbr\u003e1.6. Clinical benefits and opportunities 17\u003cbr\u003e1.7. Conclusion 18\u003cbr\u003e1.8. References 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Neural Network Models for Optimizing Rehabilitation Timelines 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNamrata KADAM and K. GAVHALE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction and background 23\u003cbr\u003e2.2. Core neural network architectures for rehabilitation 26\u003cbr\u003e2.3. Applications of neural networks in rehabilitation timeline optimization 30\u003cbr\u003e2.4. Neural network-based rehabilitation timeline optimization problems 34\u003cbr\u003e2.5. Conclusions of neural network-enhanced rehabilitation timeline optimization 36\u003cbr\u003e2.6. Conclusion 38\u003cbr\u003e2.7. References 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. AI and Digital Twins for Personalized Physiotherapy Simulations 43\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChandrakant PATIL and Swapna KAMBLE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 44\u003cbr\u003e3.2. Digital twin technology in healthcare 45\u003cbr\u003e3.3. AI foundations for physiotherapy simulations 47\u003cbr\u003e3.4. Integration of AI and digital twins for personalized physiotherapy 49\u003cbr\u003e3.5. Applications in musculoskeletal rehabilitation 51\u003cbr\u003e3.6. Applications in neurological rehabilitation 52\u003cbr\u003e3.7. Real-time monitoring and predictive analytics 54\u003cbr\u003e3.8. Challenges and ethical considerations 55\u003cbr\u003e3.9. Future directions 56\u003cbr\u003e3.10. Conclusion 58\u003cbr\u003e3.11. References 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. ML for Outcome Prediction in Orthopedic Physiotherapy 61\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePoonam PATIL and Jiwan DEHANKAR\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 62\u003cbr\u003e4.2. ML in healthcare and physiotherapy 63\u003cbr\u003e4.3. Data sources for outcome prediction in orthopedic physiotherapy 65\u003cbr\u003e4.4. ML algorithms for outcome prediction 68\u003cbr\u003e4.5. Applications in orthopedic physiotherapy 70\u003cbr\u003e4.6. Challenges and limitations 74\u003cbr\u003e4.7. Future directions and clinical implications 75\u003cbr\u003e4.8. Conclusion 76\u003cbr\u003e4.9. References 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Computer-Vision-based Fall-Risk Assessment in Physiotherapy Patients 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eT. Poovishnu DEVI and Chandrayani ROKDE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 82\u003cbr\u003e5.2. Methodologies for computer-vision-based fall-risk assessment 83\u003cbr\u003e5.3. Applications of computer-vision-based fall-risk assessment in physiotherapy 87\u003cbr\u003e5.4. Challenges and limitations 89\u003cbr\u003e5.5. Future directions and research opportunities 91\u003cbr\u003e5.6. Conclusion 94\u003cbr\u003e5.7. References 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. AI-Enhanced Virtual Reality Environments for Immersive Physiotherapy 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. ANANDH and P. BAINALWAR\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 100\u003cbr\u003e6.2. Foundations of AI and VR in physiotherapy 101\u003cbr\u003e6.3. Immersive virtual environments for rehabilitation 103\u003cbr\u003e6.4. AI algorithms for personalized physiotherapy 105\u003cbr\u003e6.7. Clinical evidence and case studies 110\u003cbr\u003e6.8. Challenges, ethical issues and limitations 111\u003cbr\u003e6.9. Future directions in AI-enhanced VR physiotherapy 113\u003cbr\u003e6.10. Conclusion 115\u003cbr\u003e6.11. References 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Predictive Modeling of Muscle Recovery Using DL 119\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuraj KANASE and Kalpana MALPE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 120\u003cbr\u003e7.2. Physiological basis of muscle recovery 121\u003cbr\u003e7.3. Traditional approaches to prediction 123\u003cbr\u003e7.4. DL techniques for predictive modeling 124\u003cbr\u003e7.5. Data sources and modalities 127\u003cbr\u003e7.6. Model architectures and frameworks 130\u003cbr\u003e7.7. Clinical applications and case studies 132\u003cbr\u003e7.8. Challenges and limitations 134\u003cbr\u003e7.9. Conclusion 136\u003cbr\u003e7.10. References 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. AI and Cloud-Based Platforms for Remote Physiotherapy Supervision 141\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSandeep SHINDE and Shamla MANTRI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 142\u003cbr\u003e8.2. AI in remote physiotherapy supervision 143\u003cbr\u003e8.3. Cloud-based platforms for telerehabilitation 146\u003cbr\u003e8.4. Synergistic integration of AI and cloud technologies 148\u003cbr\u003e8.5. Clinical applications and case studies 153\u003cbr\u003e8.6. Benefits and opportunities 155\u003cbr\u003e8.7. Challenges and limitations 157\u003cbr\u003e8.8. Future directions 159\u003cbr\u003e8.9. Conclusion 161\u003cbr\u003e8.10. References 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. ML Algorithms for Movement Quality Scoring in Physiotherapy Sessions 165\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVaishali JAGTAP and G.M. VAIDYA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 166\u003cbr\u003e9.2. Data acquisition methods for movement analysis 167\u003cbr\u003e9.3. Feature extraction and preprocessing 170\u003cbr\u003e9.4. ML algorithms for MQS 172\u003cbr\u003e9.5. Applications in physiotherapy sessions 175\u003cbr\u003e9.6. Challenges and limitations 178\u003cbr\u003e9.7. Future directions 180\u003cbr\u003e9.8. Conclusion 181\u003cbr\u003e9.9. References 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. AI-Powered Rehabilitation Robotics for Assisted Physiotherapy 185\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMandar MALAWADE and Fazil SHEIKH\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 186\u003cbr\u003e10.2. Overview of rehabilitation robotics 187\u003cbr\u003e10.3. AI in rehabilitation robotics 189\u003cbr\u003e10.4. AI techniques for assisted physiotherapy 190\u003cbr\u003e10.5. Applications in neurological and musculoskeletal rehabilitation 194\u003cbr\u003e10.6. Human–robot interaction and patient engagement 195\u003cbr\u003e10.7. IoMT and wearable integration 197\u003cbr\u003e10.8. Challenges and limitations 199\u003cbr\u003e10.9. Future directions 200\u003cbr\u003e10.10. Conclusion 201\u003cbr\u003e10.11. References 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Therapeutic Approaches in Cerebral Palsy 205\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMandar MALAWADE and G. VARADHARAJULU\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction 206\u003cbr\u003e11.2. Therapeutic approaches in cerebral palsy 208\u003cbr\u003e11.3. Neurodevelopmental therapy (NDT) 209\u003cbr\u003e11.4. Sensory integration (SI) 210\u003cbr\u003e11.5. Play therapy 211\u003cbr\u003e11.6. Combining NDT with sensory integration or play therapy 212\u003cbr\u003e11.7. Conclusion 213\u003cbr\u003e11.8. References 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12. Knowledge, Attitude and Practice of Breast Self-Examination Among Women in the Era of AI-Driven Innovations in Physiotherapy and Oncology 217\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAnkita DURGAWALE, Vaishali JAGTAP, Trupti YADAV and Rujuta NENE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1. Introduction 218\u003cbr\u003e12.2. Breast self-examination: concept, importance and current recommendations 220\u003cbr\u003e12.3. Knowledge of breast self-examination among women 222\u003cbr\u003e12.4. Attitude toward breast self-examination 223\u003cbr\u003e12.5. Practice of breast self-examination 224\u003cbr\u003e12.6. Role of AI in breast cancer screening and early detection 224\u003cbr\u003e12.7. AI-driven innovations in physiotherapy for breast cancer care 225\u003cbr\u003e12.8. Integrating AI with breast self-examination education and practice 226\u003cbr\u003e12.9. Conclusion 228\u003cbr\u003e12.10. References 228\u003c\/p\u003e \u003cp\u003eList of Authors 233\u003cbr\u003eIndex 237\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446832984344,"sku":"9781836691334","price":135.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781836691334.jpg?v=1785115195","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/ai-driven-innovations-in-physiotherapy-and-oncology-5-hardback-9781836691334","provider":"Freshly Printed Books","version":"1.0","type":"link"}