Use AI in Medicine / Course 1
Clinical AI Workflows
Design AI-assisted clinical work as an observable sequence rather than a collection of prompts.
intermediate 39 minutes 4 lessons
- Who this is for
- Physicians who already use ChatGPT or another general-purpose AI tool.
- Course outcome
- Design an AI-assisted workflow that protects clinical judgment, data quality, and accountability.
Clinical AI becomes useful when it respects sequence. This course moves from prompting to workflow architecture, then examines data discipline, local infrastructure, and the checkpoints that keep judgment visible.
Course Outcomes
What you will learn
- Distinguish an AI task from an end-to-end workflow.
- Place human checkpoints where clinical judgment belongs.
- Define the logs required before automated interpretation.
- Choose an infrastructure boundary that matches the data and use case.
Before You Begin
Prerequisites
- Routine use of at least one general-purpose AI assistant.
Ordered Syllabus
Complete the lessons in sequence.
Each article remains at its original URL. Its position here supplies the learning context.
- 01 From Tasks to Workflows Identify the coordination work that remains after an AI model completes an individual task. Distinguish a prompt, task, and workflow. Read lesson
- 02 Protect Clinical Sequence Design documentation automation around the order of clinical work and the judgment it must preserve. Map upstream context, documentation, verification, and downstream action. Read lesson
- 03 Logs Before Intelligence Establish clean, structured, timestamped ground truth before adding interpretation. Define the minimum log required by an AI-assisted workflow. Read lesson
- 04 Choose the Infrastructure Boundary Compare consumer AI with locally controlled models through privacy, governance, cost, and operational requirements. Identify when consumer AI is an inappropriate production dependency. Read lesson