5.1. Introduction

Medical imaging has long stood as a central pillar of diagnostic medicine, offering clinicians a non-invasive window into the internal anatomy and physiological functions of the human body (Jeyaseelan 2024). In fields such as oncology and physiotherapy, where understanding tissue integrity, function and pathology is crucial, imaging plays an indispensable role. Technologies such as X-rays, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and ultrasound have revolutionized the detection, characterization and monitoring of diseases, ranging from malignant tumors to soft tissue injuries. However, despite these technological advancements, conventional image interpretation still relies heavily on human expertise, which is inherently limited by cognitive bias, inter-observer variability, diagnostic fatigue and the overwhelming volume of imaging data produced in modern clinical practice (Chen and Friis 2024). In response to these limitations, the integration of artificial intelligence (AI) into medical imaging has emerged as one of the most disruptive and promising developments in contemporary healthcare. AI, particularly in the form of machine learning (ML) and deep learning (DL) algorithms, has the capacity to analyze large-scale visual datasets with unprecedented precision, speed and consistency (Rane et al. 2024). These intelligent systems are capable of learning complex patterns from annotated imaging data, performing image classification, segmentation and enhancement tasks with minimal human input and continuously improving through progressive learning. This convergence of AI and imaging has ushered in a new era of diagnostics, one that is not only more accurate and efficient but also more personalized and predictive.

In oncology, AI-driven imaging is being used to detect tumors at their earliest stages, often before symptoms manifest or traditional tools would detect them (Dubey and Sikarwar 2025). Through the application of convolutional neural networks (CNNs), AI systems can highlight suspicious regions on radiographs, identify tumor subtypes and distinguish malignant from benign lesions with high sensitivity and specificity. Moreover, AI enables automated tumor segmentation and volumetric analysis, facilitating precise treatment planning, such as in radiation therapy, where dosage and targeting are critical. AI also plays an increasing role in radiogenomics and radiomics, where quantitative imaging features are linked with molecular and genetic data to forecast prognosis and guide therapeutic choices. These capabilities position AI as a central force in the movement toward precision oncology – an approach that tailors cancer care based on individual biological profiles and treatment responses (Tippur 2023).

In the domain of physiotherapy, AI’s application is equally transformative, particularly in functional diagnostics and personalized rehabilitation. Traditional assessments of musculoskeletal conditions and physical function often rely on visual observation, patient feedback and manual measurements. AI-enhanced imaging tools now offer objective and dynamic assessments through real-time motion capture, electromyography and musculoskeletal ultrasound. These systems are capable of analyzing gait, posture, joint movement and muscle activity with exceptional accuracy, enabling early diagnosis of biomechanical dysfunctions and real-time feedback during rehabilitation (Das et al. 2022).

Furthermore, AI allows for the longitudinal tracking of patient recovery, quantifying improvements in range of motion, muscle strength and coordination. This data-driven approach enhances treatment personalization, optimizes exercise regimens and improves patient adherence and engagement.

Despite its promise, the integration of AI into diagnostic imaging is not without its challenges. Issues such as algorithmic bias, model transparency (often referred to as the “black box” problem), data privacy and the lack of standardized regulatory frameworks must be addressed to ensure the safe, ethical and equitable use of AI in clinical settings. In addition, technical barriers such as the need for large, high-quality annotated datasets and concerns regarding interoperability with existing hospital systems continue to pose hurdles to widespread adoption (Shen et al. 2025). Clinician acceptance and trust in AI systems are also crucial, as these tools are intended to augment – not replace – human expertise.

The present study aims to comprehensively examine the evolving landscape of AI-driven imaging in oncology and physiotherapy, with a focus on its diagnostic applications, clinical utility and transformative potential. It explores how AI technologies are integrated into existing imaging modalities, highlights real-world case studies and applications and evaluates the advantages and limitations associated with AI adoption (Lastrucci et al. 2024).

Furthermore, this chapter outlines the ethical, technical and regulatory considerations that accompany the use of AI in medical imaging and discusses emerging innovations that may shape the future of healthcare diagnostics.

By investigating the synergy between AI and medical imaging across two distinct yet interrelated fields – oncology and physiotherapy – this chapter seeks to underscore how these technological innovations are revolutionizing diagnostic pathways, improving clinical decision-making and advancing the goals of precision medicine.

As AI continues to evolve, its responsible and thoughtful integration into diagnostic imaging promises not only to enhance the accuracy and efficiency of disease detection but also to fundamentally transform the way clinicians approach patient care across the continuum of diagnosis, treatment and rehabilitation.