As healthcare increasingly relies on AI tools for diagnosis and treatment, even small biases in data or design can lead to unfair outcomes. This course helps healthcare professionals, product developers, and data teams understand what algorithmic bias looks like in practice and why it matters for patient safety. Learners explore real examples of bias affecting test accuracy, symptom tracking, and treatment suggestions. The course offers simple, practical ways to spot and reduce bias like checking data sources, reviewing patterns in results, and including diverse patient inputs. By the end, participants will know how to make AI systems in healthcare more accurate, inclusive, and trustworthy.