4.1.1. Background

Cancer remains one of the leading causes of morbidity and mortality worldwide, with millions of new cases diagnosed annually (Bray et al. 2018). Grateful to significant advances in early detection, surgical interventions, radiation therapy, chemotherapy and targeted drug treatments, survival rates have steadily improved across various types of cancers. As more individuals live beyond their initial diagnoses, there is a growing emphasis on enhancing their post-treatment quality of life (QoL).

Physiotherapy has emerged as a vital component of cancer rehabilitation, addressing a wide range of physical, functional and psychological challenges that survivors face (Silver et al. 2015). It aids in improving mobility, managing chronic pain, alleviating treatment-induced fatigue, preventing lymphedema and enhancing overall functional capacity. In addition, physiotherapy plays a crucial role in supporting mental well-being through structured physical activity and patient empowerment.

However, conventional physiotherapy approaches face several limitations. They are typically resource-intensive, requiring one-on-one supervision and repeated clinical visits, which can strain both healthcare systems and patients (Yan et al. 2022). Furthermore, these interventions often follow standardized protocols, lacking the personalization necessary to account for individual patient variability in health status, cancer type, treatment history and recovery trajectory. As such, innovative solutions are needed to enhance the reach, efficiency and effectiveness of physiotherapeutic care for cancer patients.

4.1.2. Rationale for AI integration

AI has demonstrated tremendous potential in transforming healthcare delivery through data-driven insights, predictive analytics and automation. In the context of physiotherapy, AI offers an unprecedented ability to personalize rehabilitation plans by analyzing individual patient profiles, including medical history, current health status and specific therapeutic needs (Pham et al. 2024). It enables the creation of dynamic, adaptive treatment protocols that evolve based on patient progress, ensuring more effective and timely interventions.

Furthermore, AI systems equipped with wearable sensors and mobile applications can monitor patient progress in real-time, capturing metrics such as range of motion, heart rate and physical activity levels. This allows healthcare providers to remotely track adherence to prescribed exercises, detect deviations from expected recovery patterns and intervene promptly when necessary. Such real-time feedback mechanisms not only enhance clinical outcomes but also empower patients by providing continuous support and motivation (Tsiouris et al. 2020).

AI’s predictive capabilities are equally valuable, as machine learning (ML) models can forecast potential complications or setbacks based on early indicators, enabling preemptive adjustments to treatment plans. Moreover, the integration of AI reduces the burden on physiotherapists by automating routine assessments and documentation, thereby allowing them to focus on delivering high-quality, hands-on care. Overall, incorporating AI into physiotherapy presents a strategic advancement that aligns with the broader goals of precision medicine and holistic cancer rehabilitation (Alagappan et al. 2024).

4.1.3. Objectives

This research aims to:

• analyze the current role of physiotherapy in cancer care and its impact on QoL for patients undergoing treatment or in remission;
  
• investigate and catalogue the various AI technologies currently used in healthcare and assess their applicability in physiotherapeutic contexts;
  
• examine the potential benefits and limitations of integrating AI into physiotherapy specifically for cancer patients, including aspects of accessibility, personalization and engagement;
  
• develop a comprehensive framework for AI-augmented physiotherapy interventions tailored to different stages of cancer treatment and recovery;
  
• encourage interdisciplinary collaboration between oncologists, physiotherapists, AI researchers and healthcare policymakers to ensure safe, effective and ethical implementation of AI technologies in rehabilitation (Shawli et al. 2024).

4.2.1. Importance of physiotherapy

Physiotherapy helps address a range of issues associated with cancer and its treatments, including pain, lymphedema, fatigue, neuropathy and decreased mobility. It also contributes to mental health by reducing anxiety and depression through physical activity.

4.3.1. ML and deep learning

ML algorithms analyze large datasets to identify patterns, classify data and make data-driven predictions (Strielkowski et al. 2023). These algorithms are highly effective in applications such as risk stratification, treatment response prediction and rehabilitation outcome forecasting. In cancer rehabilitation, ML models can be trained on patient demographics, treatment regimens and physiotherapy records to predict which interventions are likely to be most beneficial.

Deep learning, a subset of ML, uses multilayered neural networks capable of processing unstructured and high-dimensional data such as radiological scans, videos of patient movement and wearable sensor data. In physiotherapy, deep learning can detect subtle changes in posture, gait and range of motion from video recordings or sensor streams. This enables the generation of precise, automated feedback that can guide therapy adjustments. For example, convolutional neural networks (CNNs) are frequently used to analyze visual data, while recurrent neural networks (RNNs) or long short-term memory (LSTM) models are used to interpret time-series data from sensors (Tsironi et al. 2017). As these models learn from more data, they continuously improve their accuracy and adaptability, allowing for real-time, personalized care delivery in cancer rehabilitation contexts.

4.3.2. Natural language processing

Natural language processing (NLP) enables machines to understand, interpret and generate human language, making it particularly useful in the healthcare domain. In the context of cancer physiotherapy, NLP can extract meaningful insights from unstructured data sources such as electronic health records (EHRs), clinical notes, discharge summaries and patient-reported outcomes (Sim et al. 2024). This facilitates the creation of comprehensive patient profiles, helping healthcare providers design more informed and personalized rehabilitation plans.

Furthermore, NLP-powered chatbots and virtual assistants can engage with patients to gather feedback on symptoms, monitor emotional well-being and provide motivational prompts or exercise reminders. Sentiment analysis tools can also detect early signs of anxiety or depression by analyzing patient communication, allowing timely psychological interventions. NLP algorithms can summarize lengthy clinical documents, highlight key findings and ensure critical information is not overlooked during care planning.

By enhancing communication between patients and healthcare teams, streamlining documentation and supporting decision-making, NLP significantly contributes to improving the efficiency, accuracy and responsiveness of physiotherapy services for cancer patients.

4.3.3. Computer vision

Computer vision allows machines to interpret visual information, enabling real-time movement analysis and posture correction. In the context of cancer rehabilitation, computer vision is particularly useful in facilitating contactless, objective assessment of functional movement (Hellsten et al. 2021). This technology enables AI systems to track joint angles, detect gait abnormalities and evaluate exercise performance through video input or live camera feeds, thereby reducing the reliance on manual observation by therapists.

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