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AI Fundamentals in Healthcare
Plain-language concepts, myths vs. realities, everyday use cases.
A Readiness Program for Healthcare Professionals
Step into the future of healthcare with an online course designed for healthcare practitioners. Explore AI concepts, decision-making, diagnostics, patient safety, ethical safeguards and regulatory considerations through practical healthcare applications.
Step into the future of healthcare with our online course.
Designed for healthcare practitioners, the course simplifies AI concepts and shows how they can improve decision-making, diagnostics and patient safety.
Through case studies, ethical safeguards and regulatory basics, participants will learn how to adopt AI in general practice, radiology and pathology while preparing for future innovation in clinical care.
The course covers foundational AI concepts, healthcare readiness, clinical applications, safety, regulation and emerging AI technologies.
Plain-language concepts, myths vs. realities, everyday use cases.
People, process, and data readiness.
Fairness, bias, transparency, and patient safety.
Sepsis prediction, triage chatbots, readmission risk.
Amplifying care without disruption.
Chest X-rays, mammography, triage tools, workflow augmentation.
Digital slides, tumor grading, disease detection.
How to recognize red flags and apply safe boundaries.
Regulatory & institutional review.
Foundation models, multi-modal and multi-agent AI in diagnostics.
By the end of the course, participants will be able to apply the concepts across healthcare, radiology and pathology contexts.
Explain core AI concepts in accessible terms and connect them to clinical practice.
Identify practical AI use cases across general healthcare, radiology, and pathology.
Assess their organization's AI readiness using people, process, and data.
Recognize ethical and trust challenges including bias, fairness, and patient safety risks.
Interpret real-world case studies and extract lessons about successes and pitfalls.
Evaluate specialty-specific opportunities for AI in radiology and pathology workflows.
Spot early warning signs of AI failure modes and apply guardrails in practice.
Understand regulatory basics for evaluating AI vendors and ensuring compliance.
Develop an actionable plan for safe AI integration into their own professional role.
The course features an international speaker from the University of Michigan.
Professor, Department of Computational Medicine and Bioinformatics,
University of Michigan