Clinical Natural Language Processing: Reading Medical Text with AI

Most of the information in healthcare lives in unstructured text — clinician notes, discharge summaries, pathology reports, and research papers. Extracting reliable structure from that text has long been a hard problem, and modern NLP has changed what is possible. This course gives you a calm, structured introduction so you can speak confidently about clinical NLP and its uses. You will learn what clinical NLP actually does, where it succeeds, and where it still struggles with the nuance and risk that healthcare demands. The course stays grounded in widely used concepts and respects the stakes of medical work. What you'll learn: - Understand the goals of clinical NLP and how it differs from general-purpose language processing - Recognize the main families of clinical text including notes, summaries, reports, and research papers - Explore core NLP tasks including named entity recognition, relation extraction, and classification - Read how clinical ontologies and standards such as SNOMED CT and UMLS shape modeling choices - Identify the risks of clinical NLP including missed context, bias, and over-reliance on extracted data - Understand the integration points between clinical NLP, electronic health records, and research databases The course begins with the role of unstructured text in healthcare, moves through core NLP tasks and clinical standards, and closes with operational realities and risks. Written exercises help you connect each concept to realistic clinical or research scenarios. This course is designed for absolute beginners with no NLP or clinical background, including healthcare analysts, medical informatics students, and software developers entering health technology. No prerequisites are needed. The course is informational and does not provide clinical guidance; it builds the literacy that lets you ask better questions of qualified specialists.