AI in Clinical Medicine

What you'll learn
Select high-impact clinical AI use cases using a practical impact x feasibility framework
Map workflows to identify where AI truly changes decisions, timing, and workload
Define outcomes, guardrails, and equity checks that withstand governance scrutiny
Deploy LLMs safely using structured prompts, verification, and RAG grounded in policies
Evaluate imaging AI like a reviewer: prevalence traps, external validation, and failure modes.
Design patient-facing AI and monitoring pathways that avoid harm, alerts fatigue, and unsafe triage.
Requirements
There are no formal prerequisites-but to get full value, learners should come in with a solid professional baseline and the right expectations. Recommended background Clinical familiarity: you should understand how care is delivered in at least one setting (hospital, clinic, imaging, or digital care). You don't need to be a physician, but you should know what workflows, handoffs, and clinical risk feel like. Basic AI literacy: comfort with core terms like training vs inference, sensitivity vs specificity, and what an LLM is at a high level. (You don't need to code.) Quality/safety mindset: willingness to think in failure modes, escalation pathways, and "what could go wrong" rather than only "how accurate is it?" Operational thinking: interest in how tools get adopted-EHR integration, alert burden, staffing capacity, governance, and measurable outcomes.
Description
- This course contains the use of artificial intelligence -
Healthcare doesn't need more AI hype-it needs leaders who can turn AI into outcomes without creating new risks. This course is a hands-on, expert-level playbook for building and deploying clinical AI in the real world: hospitals, clinics, imaging workflows, and patient-facing digital pathways.
You will learn how modern clinical AI actually works across predictive models, imaging AI, LLMs, multimodal systems, and agent-like workflow automation. More importantly, you'll learn how to choose problems worth solving, map clinical workflows, define outcomes and guardrails (clinical, operational, safety, equity), and write use-case briefs that survive governance, IT security, and procurement.
We go deep into the realities of LLMs in healthcare: hallucinations, uncertainty, automation bias, structured prompting, and Retrieval-Augmented Generation (RAG) grounded in local policies and guidelines. You'll also master imaging AI from dataset and labeling strategy to external validation, prevalence traps, and production integration patterns such as worklist triage, second reader workflows, QA backstops, and reporting automation.
Patient-facing AI gets equal rigor: symptom triage vs coaching vs adherence support, remote monitoring at scale, alert fatigue, and high-risk mental health and neurological safeguards. Throughout the course, you'll work with real-world examples and the same frameworks used by leading health systems and digital health companies-so you can move from "interesting pilot" to safe, measurable, scalable deployment.
Who this course is for
Global and regional Market Access / HEOR / Medical Affairs leaders who need to evaluate AI-enabled clinical value claims
CMIO/CCIO teams, clinical informaticians, and digital transformation leaders
Radiology, pathology, dermatology, and ophthalmology leaders implementing imaging AI
Clinical operations and quality/safety leaders responsible for governance and outcomes
Digital health product managers building patient-facing AI and remote monitoring pathways
Healthcare data science and ML teams who want a workflow-native, deployment-first mindset
Consultants and strategy teams supporting AI adoption, vendor selection, and scaling