Authors: Md Juman Hussan
Abstract: Blood services collect a lot of data. They know who donates, when, how often, and what happens to every unit of blood after that. Most of this data still sits in spreadsheets and databases, doing very little beyond record-keeping. This paper looks at a simple, well-known framework for AI, the same one taught in introductory AI courses, and asks a plain question: what could a blood service actually do with it? Using Australian Red Cross Lifeblood as a case study, this paper walks through four uses of everyday AI: predicting which donors are about to stop donating, forecasting blood stock before shortages happen, supporting donor screening questions, and using generative AI to reach donors in their own language. Real figures from Lifeblood's published donor study, transplant program, and research investment reports are used throughout to ground the discussion in actual numbers rather than hypothetical ones. None of the ideas here need advanced or futuristic technology. They need clean data, a clear question, and a narrow tool built for one job.
