Telemedicine and AI in Physiotherapy and Oncology: Bridging Gaps in Care


7
Telemedicine and AI in Physiotherapy and Oncology: Bridging Gaps in Care



The rapid advancement of digital health technologies has fundamentally reshaped how healthcare services are delivered, particularly in specialized fields such as oncology and physiotherapy. Telemedicine and artificial intelligence (AI) have emerged as powerful tools in addressing long-standing challenges related to accessibility, continuity of care and personalization of treatment. This chapter explores how the integration of telemedicine and AI is revolutionizing care in oncology and physiotherapy, especially in resource-limited and underserved regions. Telemedicine enables the remote delivery of consultations, diagnostics, treatment supervision and rehabilitation, thereby eliminating geographical and logistical barriers. Simultaneously, AI brings computational intelligence to the forefront of care, assisting in early diagnosis through imaging, predicting treatment outcomes, customizing rehabilitation plans and enabling real-time monitoring of patients. Together, these technologies form a synergistic model that enhances the efficiency, quality and reach of healthcare services. In oncology, AI-powered imaging and predictive analytics support early detection and tailored treatment strategies, while teleoncology facilitates remote consultations, symptom monitoring and palliative care. In physiotherapy, AI algorithms analyze patient movement and guide exercise regimens, whereas telerehabilitation ensures continuity and engagement from home. This chapter also discusses implementation models from various global contexts, highlighting successful applications and measurable outcomes. Despite the promising benefits, barriers such as technological infrastructure, digital literacy, data security and regulatory challenges persist. However, with continued innovation, policy evolution and stakeholder collaboration, these hurdles can be effectively addressed.


7.1. Introduction


In the 21st century, healthcare systems around the world are undergoing a profound transformation driven by digital technologies. At the forefront of this transformation are telemedicine and artificial intelligence (AI), two pillars of innovation that are redefining how care is delivered, especially in specialized and high-demand fields such as oncology and physiotherapy (Kuo 2023). These technologies are not only improving operational efficiencies and diagnostic accuracy but are also actively bridging long-standing gaps in healthcare access, quality and continuity – challenges that have been particularly pronounced in cancer care and physical rehabilitation.


Oncology, the branch of medicine that deals with the prevention, diagnosis and treatment of cancer, faces significant hurdles in delivering equitable care (Ahmad 2025). The growing global burden of cancer, coupled with an uneven distribution of oncology specialists and healthcare infrastructure, often results in delayed diagnoses, fragmented care and poor health outcomes – particularly for patients in rural, low-income or underserved communities. Physiotherapy, a key component of comprehensive cancer care, plays a crucial role in managing the side effects of treatment, improving functional mobility and enhancing patients’ quality of life (Silver et al. 2015). However, access to qualified physiotherapists and personalized rehabilitation programs is similarly limited by geographic and systemic constraints. Telemedicine offers a practical solution to many of these challenges by enabling healthcare professionals to evaluate, diagnose and treat patients remotely using telecommunications technology. It allows continuous, real-time engagement with patients regardless of location, significantly reducing the need for in-person visits and travel. In parallel, AI has introduced a new dimension of intelligence and precision into healthcare. From detecting tumors in imaging scans to creating adaptive physiotherapy plans, AI enables data-driven decision-making that enhances both clinical outcomes and operational efficiency (Oyeniyi and Oluwaseyi 2024).


This chapter explores the convergence of telemedicine and AI in physiotherapy and oncology, arguing that their integration represents not just a technological advancement but a paradigm shift toward more accessible, personalized and value-based care. By analyzing real-world applications, technological innovations and emerging research, this study highlights how these tools are redefining patient experience and outcomes across the cancer care continuum – from diagnosis and treatment to recovery and survivorship.


Furthermore, this chapter addresses the systemic, ethical and infrastructural challenges that must be overcome to unlock the full potential of these technologies (Hoodbhoy 2023). Through this exploration, we seek to provide a comprehensive understanding of how telemedicine and AI can serve as synergistic tools for bridging critical gaps in care.


7.2. The role of telemedicine in oncology and physiotherapy


7.2.1. Definition and scope


Telemedicine refers to the use of digital communication technologies – such as video conferencing, mobile apps and wearable sensors – to deliver healthcare services remotely. It encompasses a wide range of activities, including virtual consultations, remote diagnostics, patient monitoring and follow-up care (Behar et al. 2020).


In oncology, telemedicine supports remote consultations with oncologists, remote symptom monitoring, virtual chemotherapy supervision and palliative care services. In physiotherapy, it enables remote assessments, guided exercise sessions and progress monitoring.


7.2.2. Benefits of telemedicine



  • Increased access to care: patients in remote areas gain access to specialized oncology and rehabilitation services (Parikh et al. 2021).
  • Continuity of care: enables regular follow-ups without the need for physical travel.
  • Cost reduction: decreases travel and hospital visit costs for patients and operational costs for providers.
  • Patient empowerment: encourages active patient participation in their care through digital tools.

7.2.3. Applications in oncology


Teleoncology has gained momentum in recent years. Key applications include:



  • Virtual tumor boards: multidisciplinary teams discuss cases remotely.
  • Telechemotherapy supervision: real-time monitoring of side effects during home-based chemotherapy (Cornall et al. 2024).
  • Symptom management: remote tracking of pain, fatigue and nausea through digital platforms.
  • Survivorship care: follow-up care for cancer survivors, including lifestyle counseling and mental health support.

7.2.4. Applications in physiotherapy


Telerehabilitation is the remote delivery of physiotherapy using telecommunication tools. Examples include:



  • Video-guided exercises: therapists supervise patient movements in real-time or via recorded sessions.
  • Progress tracking apps: patients log their symptoms, pain levels and adherence to exercises.
  • Wearable integration: sensors and wearables measure range of motion and physical activity levels.

7.3. AI in oncology and physiotherapy: enhancing care delivery


7.3.1. Overview of AI in healthcare


AI in healthcare involves machine learning (ML), natural language processing (NLP), computer vision and data analytics to process complex medical data (Rubinger et al. 2023). AI supports clinicians in making better-informed decisions, automates repetitive tasks and enables predictive modeling.


7.3.2. AI applications in oncology


AI has revolutionized oncology through innovations such as:



  • Medical imaging: AI models detect tumors in radiological scans (e.g. magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET)) with high sensitivity and specificity.
  • Genomic analysis: AI processes large-scale genomic data to identify mutations and suggest targeted therapies.
  • Predictive analytics: algorithms predict treatment response, recurrence and survival rates.
  • Clinical decision support systems (CDSS): AI-driven tools assist oncologists in selecting treatment protocols based on patient data.

7.3.3. AI applications in physiotherapy


In physiotherapy, AI is applied to the following:



  • Motion analysis: AI-powered systems evaluate patient movements to detect abnormalities and monitor progress (Bhamidipaty et al. 2025).
  • Personalized exercise plans: ML algorithms adjust exercise regimens based on patient feedback and performance.
  • Virtual assistants: chatbots provide exercise instructions and answer common patient questions.
  • Outcome prediction: predicts rehabilitation outcomes and guides goal-setting.

7.3.4. Integration with telemedicine


When combined with telemedicine, AI becomes a powerful enhancer:



  • Real-time feedback: AI systems analyze patient movement during telerehab sessions and provide immediate correctional feedback.
  • Triage systems: AI chatbots assess symptoms and direct patients to appropriate care levels.
  • Risk Stratification: AI identifies high-risk patients needing urgent intervention.
  • NLP: converts unstructured data from clinical notes and patient messages into actionable insights (Vashishtha and Kapoor 2023).

7.4. Case studies and implementation models


7.4.1. Remote oncology monitoring: case study from Canada


Ontario Health’s Virtual Care Program integrated teleoncology and AI-powered symptom tracking for breast cancer patients. Patients used mobile apps to report symptoms, which were analyzed by an AI algorithm that flagged urgent cases for oncologist review. Outcomes included reduced ER visits and improved symptom control.


7.4.2. AI-powered telerehabilitation in stroke survivors


A UK-based startup developed an AI platform that uses computer vision to monitor post-stroke patients during telephysiotherapy. The system analyzed joint angles and balance, providing feedback and progress reports to physiotherapists. The approach led to higher adherence and improved recovery timelines.


7.4.3. Hybrid model: India’s remote cancer rehab initiative


In rural India, where access to oncology care is limited, a non-profit organization developed a hybrid model combining teleconsultations with community-based rehabilitation aided by AI-guided mobile apps. Physiotherapists remotely supervised cancer survivors recovering from surgery or chemotherapy using locally stationed digital health workers.


7.5. Benefits of integrating AI and telemedicine in oncology and physiotherapy


The integration of AI and telemedicine represents a revolutionary approach to delivering healthcare services, especially in fields such as oncology and physiotherapy, which demand precision, continuity and personalized care (Adeghe et al. 2024). This combination not only enhances clinical outcomes but also addresses systemic issues related to accessibility, affordability and scalability. Below are key benefits with expanded discussion.


7.5.1. Improved access and equity


One of the most immediate and profound benefits of telemedicine, especially when combined with AI, is the ability to expand healthcare access to marginalized populations. Patients in remote, rural or underserved areas often lack proximity to specialized oncology centers or rehabilitation facilities. Telemedicine bridges this gap by enabling consultations, follow-ups and guided therapy sessions from the comfort of a patient’s home (George and George 2023).


When AI is layered into this model, it provides intelligent triage systems that can assess patient symptoms and prioritize cases that require urgent intervention. For instance, an AI-driven symptom checker can identify red-flag signs of disease progression in a cancer patient and prompt immediate virtual consultation.


Similarly, AI can interpret wearable sensor data to detect early signs of functional decline in a post-operative physiotherapy patient. These capabilities reduce care delays and ensure that limited healthcare resources are effectively allocated.


Moreover, AI-powered language translation and accessibility features – such as voice-guided navigation for visually impaired users – further promote health equity among linguistically and physically diverse populations (Desai et al. 2023).


7.5.2. Enhanced diagnostic accuracy

Only gold members can continue reading. Log In or Register to continue

Related posts:

Default ThumbnailAI-driven Imaging in Oncology and Physiotherapy: A Game-changer in Diagnostics Default ThumbnailIntegrating AI with Physiotherapy to Improve Quality of Life for Cancer Patients Default ThumbnailPersonalized AI-based Exercise Therapy for Oncology and Physiotherapy Patients Default ThumbnailAI-enabled Chatbots and Virtual Assistants in Oncology and Physiotherapy AI-driven Innovations in Biomaterial-based Physiotherapy and Oncology: Personalizing Patient Care Through Intelligent Monitoring and Predictive Rehabilitation Enhancing Health Data Security and Intelligence with Blockchain and Artificial Intelligence

Stay updated, free articles. Join our Telegram channel
Tags:
Mar 15, 2026 | Posted by in ONCOLOGY | Comments Off on Telemedicine and AI in Physiotherapy and Oncology: Bridging Gaps in Care

Full access? Get Clinical Tree
Get Clinical Tree app for offline access