AI in Healthcare: a Practical Introduction
Introduction to AI concepts like ML, DL, and NLP, exploring their applications in medical specialties and ethical implications in clinical practice.
Summary
Lectures are small, with a median of 8 enrolled students in the recorded sections.
What you’ll be able to do
Describe the core concepts of AI, including machine learning (ML), deep learning (DL), natural language processing (NLP), and multimodal AI.
Analyze the applications, potential, and impact of AI in various medical specialties, including radiology, pathology, cardiology, and surgery.
Evaluate the ethical and societal implications of AI in healthcare, such as bias, privacy, and the impact on the patient-physician relationship.
Appraise the strengths and limitations of AI algorithms in clinical decision-making.
Communicate effectively about AI with patients, colleagues, and the public.
Assess the public health implications of AI in healthcare, including access, equity, and community impact.
Analyze the impact of AI on population health and health disparities, and propose solutions to improve health equity.
Source: UW Guide
Grade history
↗No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course mapMED SC-M 810, 811, 812, and 813
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
An introduction to the rapidly developing field of artificial intelligence (AI) and its transformative potential in healthcare. Gain a foundational understanding of core AI concepts, including machine learning, deep learning, natural language processing, and multimodal AI. Explore the application of AI in diverse medical specialties, including radiology, pathology, cardiology, and surgery. Examine the ethical and societal implications of AI in clinical practice.
difficulty & workload
No workload feedback recorded.
Topics
- Core AI concepts: machine learning, deep learning, NLP, and multimodal AI.
- AI applications in radiology, pathology, cardiology, and surgery.
- Ethical and societal implications of AI in clinical practice.
Skills
- Describing core AI concepts including ML, DL, NLP, and multimodal AI.
- Evaluating ethical and societal implications of AI in healthcare.
- Communicating effectively about AI with diverse audiences.
Grades
Grade distribution · % of letter grades
No grades available yet.
More grade details Grade mix, volume & source data
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "167a942cd4bf9ae7c607c21e0a8833ab02d0ecc42f6fb28b4e6ed55ec8df1ea6",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}