Artificial intelligence for cancer care 4.0/5.0

Abstract

The integration of artificial intelligence (AI) in healthcare has revolutionized cancer care, enabling earlier detection, more accurate prognoses, and tailored treatment strategies. This chapter explores the transition from Healthcare 4.0 to Healthcare 5.0, highlighting the shift from a technology-centric approach to one that is deeply personalized and patient-centered. In Healthcare 5.0, we envision the focus will be on leveraging AI to empower patients and create a truly patient-centric experience. This includes using predictive modeling to anticipate treatment outcomes, employing explainable AI (XAI) to ensure transparency and trust, and utilizing cutting-edge technologies such as generative AI and digital twins to simulate patient-specific disease progressions and treatment responses. The chapter delves into the foundational machine-learning techniques used in cancer diagnosis, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. It then explores more advanced AI technologies, including generative AI, XAI, and digital twins, and discusses their applications in cancer care. Additionally, the chapter emphasizes the importance of data curation and augmentation in ensuring the availability of high-quality, diverse datasets for AI systems. It also examines computational pathology, which combines traditional pathology with AI to analyze pathology data in unprecedented ways. This chapter provides a comprehensive overview of the current and future applications of AI in cancer care, highlighting its potential to transform the way cancer is diagnosed and treated.

Authors

* External Author