Artificial intelligence (AI) is reshaping the healthcare landscape by enabling more accurate, efficient and personalized treatment strategies. This chapter examines the transformative role of AI in personalized physiotherapy and cancer care. Through the application of machine learning (ML), deep learning (DL) and advanced data analytics, AI can process and interpret large volumes of patient data – including medical imaging, genomic profiles and real-time monitoring – to inform individualized treatment plans. In physiotherapy, AI enhances motion analysis, provides virtual coaching and supports adaptive rehabilitation programs tailored to each patient’s recovery trajectory. In oncology, AI improves diagnostic precision, optimizes treatment planning and enables predictive modeling to forecast therapeutic outcomes. This therefore supports clinicians in delivering targeted interventions. The chapter explores how AI is revolutionizing traditional clinical practices, detailing its applications in real-world settings, the technological advancements driving its development and its integration into patient-centric care models. It also addresses critical challenges, such as data privacy, algorithmic fairness and regulatory hurdles, which must be resolved to ensure responsible and equitable AI adoption. Looking ahead, this chapter emphasizes AI’s potential to empower healthcare providers, enhance operational efficiency and expand access to high-quality care on a global scale. By highlighting both current achievements and ongoing challenges, this study underscores the pivotal role of AI in the evolution of personalized medicine. The application of artificial intelligence (AI) in the healthcare system has marked a new chapter on precision medicine, which is distinguished by its highly individualized treatment approaches for different conditions. AI’s influence is particularly notable in personalized physiotherapy and cancer care (Balakrishna and Solanki 2024). It has become possible for AI systems to formulate and execute tailored treatment strategies through continuous patient data analysis, real-time progress evaluation and adaptive feedback. This chapter attempts to demonstrate the impact of AI technologies when providing personalized care in physiotherapy and oncology, including technological advancement, real-world use and moral implications (Kuo 2023). AI holds significant promise in various branches of medicine, especially in personalized physiotherapy and cancer treatment. The development of AI applications in physiotherapeutic interfacing is aimed at enhancing patient recovery dynamics by shifting from static predefined exercise sequences to continuously adaptive regimes (Mikołajewska et al. 2024). These adaptive therapies incorporate the individual and their medical history, as well as age and physical limitations. AI optimization is achieved through wearable sensors, computer vision and motion analysis, which capture biomechanical feedback in real time for therapists and patients (Deshmukh 2023). These developments not only enhance the success rates of recovery but also improve engagement and interaction through tools such as virtual coaching platforms during the rehabilitation process. AI is transforming the field of oncology by improving diagnostic accuracy, refining treatment strategies and even predicting outcomes with remarkable accuracy. As a diverse and intricate ailment, cancer certainly poses a complex challenge to treat and manage. AI is actively being implemented for evaluating histopathological slides, radiological images and genomic data to identify patterns that were previously insurmountable for human perception (Jacquemin et al. 2022). Enhanced pattern recognition leads to improved primary cancer detection, precise cancer staging and accurate prognosis. Cancer treatment is also greatly benefiting from AI as it has the ability to model the response of different cancers to a given set of drugs. This enables more precise strategies to be formulated, unlike the earlier approaches that relied on guesswork (Shiwlani et al. 2023). The use of AI in healthcare personalization is made possible due to the merging of other fundamental technologies. The existence of high-scale databases containing patient histories can easily be analyzed with AI tools and algorithms to seek fine details which can assist with identifying patterns that predict specific events. With natural language processing, relevant data can be retrieved from physician notes and other non-structured information sources, transforming the healthcare system to become more modernized (Thatoi et al. 2023). These developments not only enhance clinical processes but also assist in solving knowledge deficiencies in under-researched conditions or populations. Advantages of AI in personal physiotherapy and cancer management go beyond autonomous diagnosis and monitoring. AI also assists in clinical decision-making by making recommendations based on predetermined protocols and learning from new information on a continual basis (Elhaddad and Hamam 2024). For example, in physiotherapy, predictive models can estimate the chances of re-injury or recovery stagnation as well as recovery. They can also adjust therapy plans proactively. In oncology, wearables’ real-time analytics can notify the clinician when patients are recovering differently than expected, so interventions can be made sooner. At the same time, the application of AI in medicine poses a number of significant concerns that must be addressed. These include ensuring data privacy, algorithm transparency, no bias through dataset training and stringent oversight regulations. Ethical frameworks will be essential when integrating AI into clinical services to prevent detrimental inequalities in healthcare service proliferation. This chapter looks at how AI technologies impact the fields of physiotherapy and oncology, focusing primarily on the changing dynamics of personalized care in these disciplines. We will analyze the most sophisticated technologies related to these disciplines, their practical applications, and the advantages and disadvantages that come with AI-augmented treatment processes (Bahangulu and Owusu-Berko 2025). Centering on contemporary and prospective advancements, this chapter underscores the importance of AI in facilitating greater customization and anticipation in modern healthcare systems. A number of human intellectual functions are emulated, such as learning, reasoning and problem-solving by machines, especially computer systems. In healthcare, however, AI has emerged as a disruptive and transformative force capable of improving every aspect of the care continuum, including early diagnosis and risk assessment, treatment customization and ongoing monitoring (Cacciabue and Hollnagel 2013). AI applications in healthcare encompass a number of relatively new technologies, such as natural language processing (NLP), computer vision, machine learning (ML) and deep learning (DL). NLP allows computers to read, summarize and interact with humans in language, which helps in the retrieval of relevant information from non-structured data such as clinical notes and research articles. Computer vision aids in the interpretation of medical images such as X-rays, MRIs and CT scans, thereby improving the accuracy of these diagnoses (Nagalakshmi et al. 2025). Pattern detection in information-rich datasets is possible through algorithmic approaches in ML, while higher-order feature extraction is best undertaken with DL models which imitate the architecture of the human brain, such as in tumor detection or in the classification of skin lesions. The use of AI technology crosses all areas of healthcare. Clinical assistance systems with AI technology help healthcare specialists by offering recommendations grounded in clinical data. Chatbots help in addressing commonly asked questions, reminding patients to take their medications, or even checking for symptoms which enhances patient participation and compliance. Predictive analytics powered by AI can assist in detecting individuals who may be at risk before they display clinical symptoms, making early treatment possible (Alapati and Valleru 2023). Equally important is the fact that AI is transitioning to achieve value and personalized care. It facilitates the merging of different data sources such as electronic health records, their genomic data and output from health monitoring devices, to formulate individualized treatment plans and risk profiles. AI streamlines the analysis of vast datasets, enabling real-time processing which enhances proactive care management, lessens the demand placed on healthcare providers and improves healthcare results. The development of AI technologies and their further adoption into clinical practice has the ability to transform AI’s role in the healthcare ecosystem, facilitating improved decision-making by clinicians and patients alike (Maleki Varnosfaderani and Forouzanfar 2024). Training an algorithm involves providing it with historical data so it can analyze and recognize trends to make educated predictions. Algorithms have the ability to adapt based on the information they receive; hence, there is no need to fine-tune them for every single situation through straightforward programming (Sarker 2021). As for healthcare, machine learning (ML) algorithms are best known for predicting the advancement of a certain disease, classifying a patient’s condition, automating various administrative functions and customizing treatment plans. For example, supervised learning algorithms, such as decision trees, support vector machines and logistic regression, have been successfully used in risk prediction models and diagnostic tools. Deep learning (DL), which is the most advanced type of machine learning, relies on artificial neural networks that mimic the structure of the human brain. These systems contain numerous layers that progressively extract more refined features from the raw input data. DL outperforms other systems when it comes to recognizing patterns on an extensive scale. This is particularly true when dealing with images or speech which are crucial for diagnosing medical conditions (Dargan et al. 2020). An example of this is convolutional neural networks (CNNs) which are widely used in medical imaging to accurately identify various abnormalities, including tumors, fractures and pathologies of organs. Recurrent neural networks (RNNs), which are useful in the examination of sequential data, have been useful in modeling some patients’ health trajectories over time. Furthermore, the combination of ML and DL applied in the healthcare domain has led to the creation of predictive models that can anticipate complications, streamline resource allocation and improve clinical decision-making. In the area of cancer care, DL algorithms perform pathology slide analyses and integrate genomic information to detect subtypes of cancer and forecast potential responses to treatment (Echle et al. 2021). As for physiotherapy, ML models are designed to monitor a patient’s rehabilitation progress and provide recommendations for tailoring the process using performance data that are gathered from wearable sensors. With the use of these technologies, healthcare providers can improve the productivity and outcomes for patients by ensuring more precise, timely and tailored approaches. Including environmental and lifestyle data along with genetic information means AI can create unique and effective treatment plans for patients. This approach alters the traditional care model where everyone is treated in the same manner; AI enables more accurate and advanced decision-making regarding diagnosis, prognosis and therapeutical approaches (Gupta and Kumar 2023). AI makes oncology treatment easier. For example, AI algorithms are capable of examining large genomic datasets to find mutations, expression profiles and epigenetic alterations associated with specific subtypes of cancer. This information helps to identify which therapies would be most effective for a patient’s specific condition so they do not suffer from unnecessary side effects and have better outcomes. Moreover, AI is capable of predicting how patients will respond to targeted therapies, immunotherapies and chemotherapies, using molecular biomarkers in conjunction with historical treatment data. Therefore, more accurate treatment approaches can be implemented. In physiotherapy, AI-driven precision medicine applies to developing sensor rehabilitation plans tailored to a patient’s functional deficits, milestones and comorbidities using personalized analytics (Khalid et al. 2024). AI has the ability to analyze biomechanical data obtained from motion sensors, smart wearables and computer vision, and devise tailored exercise plans for each patient. These plans can be permanently refined in accordance with real-time data, enabling therapists to manipulate intensity, duration and types of exercises for best recovery aligned with best rehabilitation practices. Moreover, AI-enabled platforms are able to actively monitor lifestyle factors such as nutrition, movement, sleep and mental health, offering comprehensive care guidelines. Such systems are capable of detecting certain behaviors that may impact recovery or treatment efficacy and have the ability to act in advance in order to optimize adherence and engagement. AI delivers actionable insights acquired from multi-modal datasets to enable healthcare professionals to optimize care and personalize care plans. This can be for preventive or therapeutic purposes, significantly improving outcomes and multispecialty patient satisfaction. Physiotherapy works to rehabilitate individuals with movement-related issues due to injury, illness or disability. Personalization in physiotherapy means creating detailed rehabilitation plans for each individual’s specific progress, needs and condition (Sadineni 2024). Different aspects of physiotherapy are being improved with AI technologies:
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The Role of Artificial Intelligence in Personalized Physiotherapy and Cancer Treatment
3.1. Introduction
3.2. Overview of AI in healthcare
3.2.1. Machine learning and deep learning
3.2.2. AI in precision medicine
3.3. Personalized physiotherapy
3.3.1. AI applications in physiotherapy
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