Healthcare AI & Clinical Informatics: From Data to Care

This course contains the use of artificial intelligence.

AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.

Artificial intelligence is moving from research papers into real clinical care - reading scans, flagging deteriorating patients, and surfacing the right information inside the electronic health record. This course teaches you how that actually works, and how to do it responsibly. You will learn the foundations of clinical informatics - how health data is structured, coded, and exchanged - and then build on it: clinical decision support, predictive analytics for risk and deterioration, AI in medical imaging, and natural-language processing over clinical notes. Crucially, you will learn the parts that separate a demo from a deployable clinical tool: rigorous validation, patient safety, FDA and Software-as-a-Medical-Device expectations, fairness and bias auditing, and the messy reality of fitting AI into clinical workflow so clinicians actually use it. Every concept is grounded in real scenarios - sepsis early warning, readmission risk, diabetic retinopathy screening, coding from notes - so you can apply it. This is not a course that hypes AI; it is honest about where it helps, where it fails, and what it takes to earn a place at the bedside. Taught for a global audience of clinicians, health-informatics and IT professionals, data scientists entering healthcare, and quality and safety leaders. By the end you will be able to evaluate, build the case for, validate, and help deploy clinical AI that is safe, fair, and genuinely useful. If you want to work at the intersection of medicine and AI, with both ambition and rigor, start here.