Technical accuracy alone rarely guarantees clinical value. This chapter argues for Human-Centered Artificial Intelligence (HCAI) as the foundation for safe, effective, and equitable healthcare AI. The chapter synthesizes global governance, European Union AI Act (EU AI Act), U.S. Food and Drug Administration (FDA) Good Machine Learning Practice (GMLP) and Predetermined Change Control Plan (PCCP), National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), World Health Organization (WHO) ethics guidance, and National Health Service/National Institute for Health and Care Excellence (NHS/NICE) frameworks, into actionable design and evaluation guidance that prioritizes usability, trust, equity, and lifecycle oversight. Drawing on real-world successes and failures (For example, sepsis prediction, ambient “Smart ICU,” radiology augmentation), it shows how neglecting human factors leads to alert fatigue, overreliance, or erosion of trust. This chapter contributes two practical tools: (1) the CARES Framework (Co-Design, Assess, Rollout, Evaluate and Evolve, Share) to operationalize HCAI across the product lifecycle; and (2) a healthcare-specific HCAI Maturity Model to assess organizational readiness across governance, safety, equity, workforce, and data infrastructure. CARES is presented as an original synthesis informed by global standards (EU AI Act, FDA GMLP/PCCP, NIST AI RMF, WHO, NHS/NICE) and is distinguished from existing models by its explicit coupling of participatory co-design, pre-scale usability/equity validation, and post-deployment sociotechnical auditing. The chapter concludes with strategic recommendations for clinicians, health systems, vendors, and policymakers to embed HCAI as routine practice, so that AI augments human expertise, explains its limits, and evolves under transparent governance.