The rapid advancements in Internet of Things (IoT) technologies, wearable biosensors, and cloud computing have paved the way for revolutionary changes in cancer diagnosis and treatment. This chapter explores the integration of IoT-enabled real-time cancer monitoring systems, focusing on the utilization of biosensors and cloud intelligence for continuous patient health surveillance. The seamless connection between wearable sensors and cloud-based platforms allows for the continuous acquisition, storage, and analysis of critical cancer biomarkers, facilitating early-stage detection and personalized treatment strategies. Machine learning and artificial intelligence algorithms play a pivotal role in enhancing diagnostic accuracy by processing complex health data, enabling timely intervention and treatment optimization. The chapter also addresses key challenges related to the security, reliability, and scalability of these technologies, alongside the opportunities they present for improving patient outcomes and reducing healthcare burdens. By combining the latest in AI, IoT, biosensors, and cloud intelligence, this chapter presents a comprehensive view of the future of cancer care, highlighting its potential to transform early detection, patient management, and treatment personalization. The implementation of these technologies could ultimately lead to more precise, cost-effective, and accessible cancer healthcare worldwide.
The healthcare industry is undergoing a significant transformation due to the rapid advancements in technology, particularly through the integration of the Internet of Things (IoT), wearable biosensors, and cloud-based platforms [1]. These innovations have the potential to revolutionize cancer monitoring and treatment, offering more precise, real-time solutions for early diagnosis, continuous monitoring, and personalized care [2]. Cancer remains one of the leading causes of death worldwide, and the need for more efficient and accessible healthcare solutions is critical [3]. Traditional cancer detection methods, such as imaging and biopsies, often rely on periodic assessments, which may not capture subtle changes in a patient's condition or detect the disease at an early stage [4]. IoT-based systems, paired with biosensors, offer an opportunity to bridge this gap by providing continuous monitoring, enabling early intervention, and reducing the need for frequent hospital visits [5].
Wearable biosensors, which can detect cancer biomarkers in real-time, have emerged as a powerful tool in this new era of healthcare [6]. These devices are capable of monitoring a wide range of biological parameters, including proteins, nucleic acids, and metabolites, that indicate the presence or progression of cancer [7]. By continuously tracking these biomarkers, IoT systems can provide healthcare professionals with up-to-date information on a patient's condition, helping them detect abnormalities that may be indicative of cancer [8]. This real-time data transmission, often facilitated through wireless communication networks, enables timely responses and better management of the disease [9]. The continuous stream of data not only aids in early detection but also allows healthcare providers to monitor treatment efficacy and make necessary adjustments quickly, ensuring that the patient is receiving the best possible care [10].
One of the most promising applications of IoT-enabled cancer monitoring systems is in the realm of personalized treatment [11]. Traditionally, cancer treatments such as chemotherapy, radiotherapy, and immunotherapy are prescribed based on standard protocols, which do not always account for the unique characteristics of an individual’s disease [12]. By leveraging continuous, real-time data collected through wearable biosensors, healthcare providers can tailor treatment strategies to a patient's specific needs [13]. AI and machine learning algorithms can analyze this data, identify patterns, and predict how a patient will respond to different therapies. This data-driven approach allows for dynamic adjustments to the treatment plan, optimizing the therapy to achieve better results and minimize side effects [14]. Personalized treatment not only improves patient outcomes but also reduces the overall burden on the healthcare system, as it focuses on providing targeted, effective care rather than broad-spectrum treatments [15].
