AI can help interpret images, identify patterns, organize clinical information, and accelerate parts of scientific discovery. A useful model output is not the same as a safe treatment: laboratory work, clinical trials, regulation, and clinician judgment still matter.
02Show meFollow the connection
01Medical or biological data
02Model
03Candidate or signal
04Validation
05Clinical use
Here's what matters
The answer changes when these conditions change.
01Was the result validated outside the training data?
02Is the intended use specific and regulated?
03Who is represented in the data?
04Does performance hold in real clinical settings?
05Does a clinician remain accountable?
03Prove itOpen the machinery
Inspect the claims behind this answer.
Each layer shows evidence type, geography, assumptions, caveats, review date, and original sources.
Observed
In the blind CASP14 assessment, AlphaFold predicted protein structures with accuracy competitive with experimental structures in a majority of evaluated cases.
This made an important scientific bottleneck more tractable; it did not replace laboratory validation or cure a disease.
Observed
The FDA maintains a growing list of AI-enabled medical devices authorized for U.S. marketing after applicable premarket review.
This is real clinical infrastructure, but authorization is device- and intended-use-specific.