Every conversation we have about AI in human factors engineering eventually arrives at the same tension: this is a discipline built on protecting patients and users from harm, and also a discipline drowning in repetitive documentation work. Both things are true, and the honest answer to "should we use AI here?" depends entirely on which part of the program you're talking about.
Where AI genuinely helps
- First drafts of structured documentation. Turning task analysis notes into a draft use scenario, or formative session notes into a preliminary findings summary, is exactly the kind of transformation AI does well — and it saves engineers hours per study.
- Consistency checking. Flagging when a critical task in the risk analysis has no corresponding test evidence, or when terminology drifts between documents, is a pattern-matching problem AI is well suited for.
- Literature and precedent search. Surfacing similar use errors from comparable device types, or relevant sections of FDA guidance, speeds up the research an engineer would otherwise do manually.
Where it shouldn't make the call
- Severity and risk judgments. Deciding how serious a potential use error is requires clinical and contextual judgment that shouldn't be delegated to a model, however well it drafts text.
- Root-cause interpretation of observed use errors. Understanding why a participant struggled — a labeling issue versus a training gap versus a genuine design flaw — needs a human who watched the session and can read the nuance.
- Final sign-off on submission content. Anything that becomes part of a regulatory submission needs a named, qualified reviewer who takes ownership of its accuracy.
The goal isn't an AI that writes your HFE report. It's an AI that clears the documentation debris so your engineers spend their time on the judgment calls only they can make.
The design principle we build around
Every AI-assisted feature in HF Workspace follows the same rule: AI drafts, humans decide. Draft content is visibly marked as unreviewed until a qualified human factors engineer edits and approves it, and that review is recorded — who reviewed it, when, and what changed. That audit trail matters as much as the acceleration; a regulator reading your file should be able to see exactly where human judgment was applied, not just trust that it was.
A practical starting point
If you're evaluating where to introduce AI assistance into your own HFE process, start with the steps that are purely transformational — turning existing, human-generated inputs into structured drafts — before touching anything that involves judgment about risk or harm. That ordering keeps the benefits real and the risk to your submission low.
