Abstract
<jats:p>Artificial intelligence (AI) is disrupting healthcare in the form of disease diagnostics, clinical decision support, and remote patient monitoring. Nevertheless, such developments raise many ethical issues regarding patient safety, data sensitivity, and fair access. This chapter focuses on AI in healthcare as a process that is guided by the principles of clinical validation, transparency, and constant monitoring to avoid diagnostic mistakes and algorithmic bias. It discusses privacy-protective solutions like blockchain governance, federated learning, and differential privacy to ensure sensitive health information is not leaked. Moreover, it states that inclusive datasets and models of fairness are essential to providing equitable access, especially in low- and middle-income countries. Based on the case studies of NHS AI Lab (UK), Mayo Clinic (USA) and AIoT-based diabetes monitoring (Singapore), the chapter suggests an ethical AI implementation system that considers both innovation and trust to provide safe, confidential, and equity healthcare to all populations.</jats:p>