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We're working with a leading AI research lab to build rigorous evaluations of how well AI reads real radiology studies, and we need practicing radiologists to define what "correct" actually looks like. AI can describe an image fluently; whether it gets the clinically load-bearing call right — the measurement by the correct convention, the interval change against a prior, the structured category in a borderline case — is where expert judgment is scarce and where you come in.
This is not image labeling. It's the reasoning you already do every day, captured as a reference standard that trains and tests frontier models.
What you'd do
- Read de-identified studies (CT, MR, radiography, ultrasound, PET/CT, mammography, and more) and write the reference report or the specific finding.
- Apply the structured systems and measurement conventions you use in practice (RECIST, Lung-RADS, BI-RADS, CAD-RADS, ASPECTS, and others)
- Review AI-drafted reads and mark what's inaccurate, unsupported, or unsafe.
- Contribute the kinds of cases that stump a strong model.
Who we're looking for
- Board-certified or board-eligible radiologists (ABR, FRCR, EDiR, or international equivalent). Residents and fellows in later training may be considered.
- Any diagnostic sub-specialty: neuro, chest, abdominal, MSK, breast, cardiac, nuclear medicine, paediatric, and others.
- Currently reading studies, or recently in active practice.
Listing sourced from Mercor. Annotation Academy is independent of these platforms and does not guarantee work or pay. See our disclosures.