This session shows how enterprise-grade responsible AI principles can be translated into safe, effective healthcare deployments through real-world experience building client-facing medical management algorithms. We walk through the full lifecycle of clinical algorithm development — from data provenance and clinical validation to bias assessment, explainability, and human‑in‑the‑loop workflows — illustrating how governance structures and AI literacy programs become practical enablers rather than compliance tasks. Attendees will leave with a pragmatic playbook of governance steps, stakeholder engagement approaches, and monitoring practices that make healthcare AI trustworthy in practice.
