AI-driven Innovations in Physiotherapy and Oncology 4

1. Auflage 2026
ISBN: 978-1-83669-126-6
Verlag: ISTE Ltd

Buch, Englisch, 256 Seiten

Reihe: ISTE Invoiced

AI-driven Innovations in Physiotherapy and Oncology 4 explores how artificial intelligence (AI) is transforming patient care, rehabilitation and cancer management. It presents a comprehensive overview of AI applications in physiotherapy and oncology, highlighting data-driven diagnostics, personalized treatment planning and predictive analytics.

This book discusses the integration of machine learning, computer vision and wearable technologies to enhance rehabilitation outcomes, monitor patient progress and optimize therapeutic interventions. In oncology, it also examines AI-supported early detection, tumor classification, precision medicine and treatment response prediction. Real-world case studies and emerging research trends are included to demonstrate practical implementation and clinical impact.

By bridging technological advancements with healthcare practices, this book provides valuable insights for researchers, clinicians and professionals seeking to understand and leverage AI for improved patient outcomes and next-generation medical innovation.

Autoren/Hrsg.

Kumar, Abhishek

Batta, Priya

Ahuja, Sachin

Rathore, Pramod Singh

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Krankenhausmanagement, Praxismanagement
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface xvii
Abhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHORE

Introduction xix
Abhishek KUMAR, Priya BATTA, Sachin AHUJA and Pramod Singh RATHORE

Chapter 1. ML for Predictive Recovery Models in Physiotherapy 1
Suraj KANASE and G.M. VAIDYA

1.1. Introduction 1
1.2. Overview of ML in healthcare 3
1.3. ML techniques for predictive recovery in physiotherapy 4
1.4. Applications of ML in physiotherapy 7
1.5. Integration of ML with digital health tools 10
1.6. Challenges in implementing ML predictive models 12
1.7. Future directions 14
1.8. Conclusion 16
1.9. References 16

Chapter 2. AI-Powered Gait Analysis for Post-Injury Rehabilitation 21
Pragati SALUNKHE and K. GAVHALE

2.1. Introduction 21
2.2. Fundamentals of gait analysis 23
2.3. Conventional methods and their limitations 25
2.4. AI techniques in gait analysis 27
2.5. Wearable and vision-based systems 30
2.6. Applications in post-injury rehabilitation 32
2.7. Clinical integration and telerehabilitation 34
2.8. Challenges and future directions 36
2.9. Conclusion 37
2.10. References 38

Chapter 3. DL Applications in Automated Physiotherapy Progress Tracking 41
Ankita DURGAWALE and Fazil SHEIKH

3.1. Introduction 42
3.2. Foundations of DL in healthcare 43
3.3. Physiotherapy and the need for automated progress tracking 45
3.4. Computer vision approaches for physiotherapy monitoring 47
3.5. wearable sensor integration and DL models 49
3.6. Multimodal data fusion for comprehensive progress tracking 50
3.7. Personalization and adaptive DL in physiotherapy 53
3.8. Telerehabilitation and remote monitoring applications 54
3.9. Challenges in clinical integration 56
3.10. Conclusion 57
3.11. References 58

Chapter 4. Real-Time AI Feedback Systems for Postural Correction in Physiotherapy 61
Smita PATIL and Rasika Ranjit CHAFLE

4.1. Introduction 62
4.2. The role of posture in physiotherapy 62
4.3. Evolution of AI in physiotherapy 64
4.4. Real-time AI feedback systems: mechanisms and technologies 66
4.5. Applications in postural correction 69
4.6. Wearables, sensors and vision-based AI approaches 71
4.7. Benefits in clinical and home-based physiotherapy 72
4.8. Challenges and limitations 74
4.9. Future directions 76
4.10. Conclusion 77
4.11. References 78

Chapter 5. Wearable Sensor Data and ML for Remote Physiotherapy Monitoring 81
Sandeep SHINDE and Shamla MANTRI

5.1. Introduction 82
5.2. Wearable sensors for remote physiotherapy monitoring 83
5.3. ML for sensor data interpretation in remote physiotherapy 87
5.4. Applications and case studies of wearable sensor data and ML in remote physiotherapy 90
5.5. Challenges and future directions 93
5.6. Conclusion 96
5.7. References 96

Chapter 6. AI-Driven Decision Support Systems for Personalized Physiotherapy Plans 101
Shraddha MOHITE and P. BAINALWAR

6.1. Introduction 101
6.2. Foundations of AI-DSS 103
6.3. Applications of AI-DSS in personalized physiotherapy 106
6.4. Clinical benefits and patient outcomes 109
6.5. Challenges and limitations 111
6.6. Future directions 113
6.7. Conclusion 116
6.8. References 116

Chapter 7. Reinforcement Learning for Adaptive Exercise Prescription in Rehabilitation 121
Omkar SOMADE and Chandrayani ROKDE

7.1. Introduction 122
7.2. RL foundations for rehabilitation. 124
7.3. Robotic rehabilitation applications 127
7.4. Beyond robotics: chronic disease and treatment regimes 129
7.5. Sensor integration and data-driven RL 131
7.6. Neuromechanical adaptations, ethics, challenges and future directions 134
7.7. Conclusion 136
7.8. References 136

Chapter 8. Computer Vision and AI for Automated Joint Range-of-Motion Assessment 141
T. Poovishnu DEVI and Jiwan DEHANKAR

8.1. Introduction 141
8.2. Traditional approaches to ROM assessment 144
8.3. CV-based ROM assessment 147
8.4. Clinical applications of CV and AI for ROM assessment 149
8.5. Challenges and ethical concerns in automated ROM assessment 151
8.6. Future directions in AI and CV for ROM assessment 154
8.7. Conclusion 157
8.8. References 157

Chapter 9. ML Models for Early Detection of Movement Disorders in Physiotherapy 161
S. ANANDH and Swapna KAMBLE

9.1. Introduction 162
9.2. Overview of movement disorders in physiotherapy 163
9.3. ML approaches in movement disorder detection 166
9.4. Data sources and feature extraction in physiotherapy 168
9.5. Clinical applications in physiotherapy 172
9.6. Challenges, limitations and ethical considerations 175
9.7. Conclusion 177
9.8. References 178

Chapter 10. AI-Based Predictive Analytics for Sports Injury Prevention and Recovery 181
Vaishali JAGTAP and Kalpana MALPE

10.1. Introduction 182
10.2. Data sources for predictive analytics in sports 183
10.3. AI techniques in sports injury prediction 186
10.4. Applications in injury prevention 188
10.5. Applications in injury recovery and rehabilitation 190
10.6. Ethical, practical and implementation challenges 192
10.7. Future directions 195
10.8. Conclusion 197
10.9. References 197

List of Authors 201
Index 205

Über Autor(innen)

Abhishek Kumar is an assistant director and professor in the Department of Computer Science and Engineering at Chandigarh University, Mohali, India. His expertise spans AI, renewable energy and image processing.

Priya Batta is an associate professor at Amity School of Engineering and Technology, Amity University Punjab, Mohali, India. Her research specializes in AI, blockchain and the IoT.

Sachin Ahuja is the Executive Director of Engineering and a professor at Chandigarh University, Mohali, India. His research specializes in AI, machine learning and data mining.

Pramod Singh Rathoreis an assistant professor at Manipal University Jaipur, India. His research interests include NS2, networks, data mining and DBMS.