9.1.1. Background

The increasing complexity of cancer treatment and the multifaceted nature of physiotherapy underscore the need for more intelligent, adaptable and data-driven healthcare solutions. Traditional clinical approaches, while effective in many respects, often rely on generalized treatment protocols that may not fully accommodate the unique physiological and recovery needs of individual patients (Beutler et al. 2016). These standardized models can fall short in addressing patient-specific variables such as genetic makeup, tumor heterogeneity, comorbidities and variations in treatment response and rehabilitation potential.

The advent of artificial intelligence (AI), particularly neural networks, presents a promising paradigm shift. Neural networks, modeled after the interconnected structure of neurons in the human brain, possess the ability to learn from vast and diverse datasets, identify complex and often imperceptible patterns and make predictive decisions with high accuracy. Unlike rule-based systems, neural networks improve over time as they are exposed to more data, allowing for dynamic adaptation and continuous learning. These features make them especially valuable in healthcare domains where precision and personalization are paramount.

In oncology, neural networks have demonstrated exceptional promise in enhancing diagnostic imaging, predicting treatment outcomes and stratifying patient risk with unprecedented granularity (Khalighi et al. 2024). For instance, convolutional neural networks (CNNs) are increasingly used to analyze radiological images such as CT scans, MRIs and PET scans, facilitating early detection of malignancies and reducing diagnostic errors. Similarly, recurrent neural networks (RNNs) are being used to model disease progression and support long-term treatment planning through temporal data analysis.

In physiotherapy, neural networks enable personalized rehabilitation programs by processing real-time patient data from wearable sensors, motion capture systems and electronic health records. They can detect subtle deviations in movement patterns, assess recovery progress and suggest adjustments to therapy regimens to optimize outcomes (Harbourne and Stergiou 2009). Moreover, neural networks can integrate data from multiple modalities – biomechanical, physiological and behavioral – to create a holistic view of patient health, thereby supporting more informed and responsive clinical decisions.

The convergence of AI and healthcare, particularly through neural networks, signals a transformative era wherein treatment strategies are no longer confined to one-size-fits-all models but are instead tailored to the individual (Garg et al. 2025). This shift holds significant promise for oncology and physiotherapy, two disciplines that demand high levels of personalization and precision. As research and technology continue to evolve, the integration of neural networks into these fields is poised to not only enhance clinical effectiveness but also improve patient quality of life and long-term health outcomes.

9.1.2. Significance of neural networks in healthcare

In recent years, neural networks have increasingly been incorporated into clinical practice due to their superior ability to process complex, high-dimensional and nonlinear medical data. As healthcare systems around the world strive to deliver more precise, efficient and personalized care, neural networks have emerged as a cornerstone of modern medical innovation, particularly in fields requiring intricate data interpretation such as oncology and physiotherapy (Thacharodi et al. 2024).

In oncology, neural networks have revolutionized diagnostic processes by significantly improving the accuracy and speed of image analysis. Convolutional neural networks (CNNs), in particular, excel in analyzing radiological images – including MRI, CT and PET scans – by detecting minute abnormalities that may be overlooked by human observers. These networks are capable of identifying malignancies at earlier stages, classifying tumors based on subtle histopathological features and differentiating between benign and malignant lesions with high sensitivity and specificity. Moreover, they are instrumental in genomic-based oncology, where deep learning models analyze vast amounts of genetic and molecular data to predict tumor behavior, therapeutic response and potential for metastasis (Ortega-Batista et al. 2025). This capability enhances personalized medicine by aligning treatment plans with the unique genetic profile of each patient.

Beyond diagnostics, neural networks also support prognostic modeling and treatment optimization. By integrating clinical data, imaging results and molecular profiles, these systems can forecast disease progression, estimate survival rates and assist in the selection of the most effective therapy regimens. This predictive power enables oncologists to make informed decisions, reduce overtreatment or undertreatment, and improve overall patient outcomes (Walsh et al. 2019).

In physiotherapy, neural networks play a pivotal role in the development of individualized rehabilitation protocols. Using data from motion capture systems, inertial measurement units (IMUs), wearable sensors and real-time patient feedback, these networks assess biomechanical function, identify compensatory movement patterns and monitor recovery trajectories. By recognizing deviations from optimal motion or strength benchmarks, neural networks provide actionable insights that help therapists adjust interventions dynamically and with greater precision.

9.2.1. Basics of neural networks

Neural networks are sophisticated computational frameworks inspired by the architecture of the human brain (Parhi and Unnikrishnan 2020). They consist of multiple layers of interconnected processing units, known as neurons, which simulate biological neural activity. Each neuron receives input, applies a weighted transformation, passes the result through an activation function and transmits the output to neurons in the subsequent layer. Through iterative training and backpropagation, neural networks learn to identify complex, nonlinear patterns in large datasets, making them particularly suited for diverse applications in healthcare.

9.2.2. Types of neural networks in healthcare

Various neural network architectures are used in healthcare, each tailored to specific types of data and clinical objectives:

• Convolutional neural networks (CNNs): CNNs excel in image-related tasks and are extensively used in medical imaging for disease detection, segmentation and classification of radiographic data such as MRI, CT and X-rays.
  
• Recurrent neural networks (RNNs): RNNs are well-suited for sequential or time-series data. In healthcare, they are instrumental in modeling patient recovery trajectories, monitoring vital signs over time and predicting disease progression (Li-wei et al. 2014).
  
• Deep neural networks (DNNs): these are multi-layered networks capable of processing high-dimensional and complex data. In personalized medicine, DNNs facilitate the integration of genomic, clinical and lifestyle data to tailor treatments to individual patients.
  
• Generative adversarial networks (GANs): GANs are used to generate synthetic medical images for training purposes and to augment limited datasets. They enhance the robustness of diagnostic algorithms by simulating diverse pathological conditions.

9.2.3. Integration with healthcare systems

The integration of neural networks into healthcare infrastructure is transforming clinical workflows (Maleki Varnosfaderani and Forouzanfar 2024). These models are embedded within electronic health records (EHRs), diagnostic imaging platforms and wearable health monitoring devices. By enabling real-time data analysis and predictive modeling, neural networks support clinical decision-making, automate routine tasks and personalize patient care. Their deployment across various domains – from diagnostics and therapeutics to patient monitoring and administrative functions – highlights their potential to improve efficiency, accuracy and outcomes in modern healthcare systems.

9.3.1. Early detection and diagnosis

Early detection significantly improves cancer prognosis. Convolutional neural networks (CNNs) have emerged as powerful tools in identifying malignancies at their nascent stages by analyzing complex radiological data such as CT scans, MRIs and PET images. These networks learn from vast annotated datasets to detect abnormal tissue patterns with remarkable precision, often matching or surpassing the diagnostic accuracy of expert radiologists (Singh et al. 2024).

9.3.2. Tumor classification and staging

Accurate classification and staging of tumors are vital for determining prognosis and tailoring therapeutic approaches. Neural networks, especially those incorporating multi-modal data, can integrate imaging results with histopathological and genomic information. This comprehensive data fusion enables more precise differentiation between benign and malignant lesions, tumor subtypes and cancer stages, supporting more nuanced and effective clinical interventions (Steyaert et al. 2023).

For example, hybrid models combining CNNs with genomic data have shown promise in classifying gliomas into subtypes associated with distinct molecular profiles, influencing both prognosis and therapy selection.

9.3.3. Treatment personalization

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