Input: approved device copy and one training step
Output: a patient and clinician explainer
Aspect: 16:9 and 9:16
Build log
1. Set the rule
I opened the approved patient materials and reviewer notes, locked clinician-approved wording, and wrote down what could not change.
2. Run one minimum version
I made one small a patient and clinician explainer first. The device shape changed, so I locked the reference and kept the camera still.
3. Build the focused variant
I reused the same reference and changed only one device close-up and one approved use step.
4. Review and export
I checked approved wording, readability, and no medical overreach at full size, then exported the final 9:16 and 16:9.
Complete execution record
Task boundary and source gate
Treat AI medical device explainer video as a deliverable, not a definition. The job receives approved device copy and one training step and owes a patient and clinician explainer in 16:9 and 9:16. Write the rejection reason first, then turn it into the first hard constraint.
Require approved wording, audience, range of motion or usage boundary, contraindications, reviewer, and the source document. Keep patient and clinician versions separate. Do not diagnose, promise recovery, or convert a general health topic into personal advice. Medical content is a controlled workflow, not a visual experiment.
The imgmov route
Start with approved patient or clinician copy. Canvas holds one action per shot. The external editor adds caution, duration, when to contact a professional, and reviewer version. Generation never adds a benefit.
MedlinePlus advises discussing health information with a health care provider before relying on it and states that it does not replace professional medical care. Keep the approved source sentence and reviewer version next to the media.
Lock the variables that cannot drift
Lock approved sentences, dosage or duration wording, range, device appearance, body position, clothing, background, and caution placement.
Put the locked fields at the top of the prompt, not at the end: The practical move is to lock clinician-approved wording and change only one device close-up and one approved use step. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Set the rule: I opened the approved patient materials and reviewer notes, locked clinician-approved wording, and wrote down what could not change. Leave one checkable artifact from this step; do not start the next until it exists. 2) Run one minimum version: I made one small a patient and clinician explainer first. The device shape changed, so I locked the reference and kept the camera still. Leave one checkable artifact from this step; do not start the next until it exists. 3) Build the focused variant: I reused the same reference and changed only one device close-up and one approved use step. Leave one checkable artifact from this step; do not start the next until it exists. 4) Review and export: I checked approved wording, readability, and no medical overreach at full size, then exported the final 9:16 and 16:9. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
Check that the action stays within the approved range, captions match approved copy, and the viewer knows what not to do without reading a disclaimer twice.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
If motion exceeds range, shorten the clip or replace it with a still diagram. If phrasing shifts, restore the approved sentence and send the uncertainty to the reviewer.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
Versioning and handoff
Every channel keeps the same approved caution, source document ID, reviewer version, and contact path.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media.
Do not diagnose, promise recovery, or convert a general health topic into personal advice. Medical content is a controlled workflow, not a visual experiment. If the request only asks what ai medical device explainer video means, a search page is faster. This page is useful when someone must deliver a patient and clinician explainer under real constraints.
Frequently asked questions
What is the one rule I keep repeating?
The practical move is to lock clinician-approved wording and change only one device close-up and one approved use step.
The workflow is specific about its stopping point: it starts with approved device copy and one training step and stops at a patient and clinician explainer. In imgmov, upload the source, lock it as a reference, set 16:9 and 9:16, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
What do I check before export?
Approved text, one action, product/device shape, and medical claims.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
What makes this different from a generic generator?
I start from approved device copy and one training step and keep a patient and clinician explainer consistent, instead of inventing a new scene each run.
The check is not “does it look AI-nice?” It is: Check that the action stays within the approved range, captions match approved copy, and the viewer knows what not to do without reading a disclaimer twice. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
What should I do if the first render drifts?
Cut the motion, lock the reference, and rerun one small version before batching.
If motion exceeds range, shorten the clip or replace it with a still diagram. If phrasing shifts, restore the approved sentence and send the uncertainty to the reviewer. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.