AI and Big Data: Pioneering the Next Generation of Health Care Solutions in Cancer Treatment

Abstract

One of the biggest causes of illness and death on the globe, cancer continues to be a problem for healthcare systems. From early detection and diagnosis to treatment planning and long-term patient monitoring, recent developments in artificial intelligence (AI) and big data technologies have brought forth revolutionary possibilities in the field of cancer care. This chapter explores the pivotal role of AI and Big Data in revolutionising oncology by providing innovative, data-driven health solutions aimed at improving patient outcomes and optimising healthcare delivery. This chapter's main goal is to give an overview of how AI and Big Data are changing cancer research, diagnosis, treatment, and patient care. It starts by providing basic information about cancer, such as its definitions, the main types (solid tumours, hematologic malignancies, and rare cancers), and statistics on incidence, survival rates, and healthcare burden as of right now. Building upon this foundation, the chapter delves into the critical sources of Big Data in oncology—such as Electronic Health Records (EHRs), genomic databases, clinical trials, and patient-reported outcomes, and addresses the challenges of integrating and managing these vast data sets. The chapter further investigates the application of AI in cancer diagnosis through advanced imaging analysis, machine learning models for early detection, and risk prediction tools. It highlights how AI facilitates personalised treatment planning by enabling genomic profiling, biomarker discovery, and the development of clinical decision support systems. Patient monitoring is also examined, showcasing the role of remote technologies, wearable devices, and telemedicine in enhancing quality of life and symptom management for cancer patients. Ethical considerations, including data privacy, algorithmic bias, and the regulatory landscape, are critically analysed to ensure responsible AI implementation. The chapter concludes with a discussion on future innovations—such as Natural Language Processing (NLP), next-generation machine learning techniques, and AI’s potential role in drug development—and presents real-world case studies demonstrating successful integration of AI and Big Data in cancer care. The main aim of this chapter is to emphasise the transformative potential of AI and Big Data in oncology while advocating for continuous research, interdisciplinary collaboration, and a strong patient-centric approach. It calls on healthcare professionals, technologists, researchers, and policymakers to work together in harnessing these technologies for more equitable, efficient, and effective cancer care in the future.

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