Radiology AI training data, read by board-certified radiologists.
Imaging models get judged by radiologists in the end, so they should learn from radiologists at the start. eeko commissions certified readers to label studies, draw segmentations, grade generated reports, and second-read each other's work. Study-level context, consent on every item, and a signed record of who read what.
What is radiology AI training data?
Radiology AI training data is the labeled imaging a model learns from and is scored against: a CT study with each finding marked and its side confirmed, a lesion outlined on the right series and phase, a report tagged for negation and uncertainty, a model-written report graded against the pixels it claims to describe. When those labels come from a crowd working on context-free slices, the model learns the wrong lesson, and a radiologist will spot it on the first case.
The failures are characteristic: a finding placed on the wrong side, a nodule skipped because the task was something else, a measurement pulled from the wrong sequence, a generated report that argues with its own images. eeko's data practice commissions credential-checked radiologists to label at the study and series level, then has a second radiologist adjudicate before anything ships.
This page belongs to eeko's human-made AI training data practice, built because the imaging and clinical systems we deliver for providers (see our healthcare AI work) need labels nobody has published. See also training data for healthcare and how eeko verifies human data.
Where imaging models go wrong, and what fixes it.
Three failure patterns from imaging model evaluations, and the labeling decision behind each fix.
Medical imaging annotation and datasets, to your schema.
Six request types we fill for imaging teams, each one starting with a labeling guide, then a pilot, at a fixed per-study price.
- Subspecialty-matched readers
- Part of our data annotation services
- Masks, boxes, and keypoints
- See image datasets
- Entity and relation labels
- Builds on document datasets
- Error taxonomy agreed up front
- Feeds RLHF preference data
- Consent bound per study
- Exclusive licence available
- Fingerprinted against contamination
- See AI evaluation benchmarks
From labeling guide to signed delivery.
Pilot before volume, fixed price per study, and a radiologist on both ends of every label.
Every study, provable.
Imaging AI gets audited. The labels behind it should survive the audit.
What travels with each study
- Fingerprint — frame and perceptual hashes taken on arrival and compared across the set for duplicates.
- Signed provenance — the labeling reader, the adjudicator, and any model-suggested label, on every item.
- Independent timestamp — proof of when the study and its labels existed, from a source we do not control.
- Consent and licence — recorded per study, so you can show what each one may be used for.
- Privacy and origin checks — report text screened and masked; generator signatures and C2PA or IPTC fields detected.
- Audit trail and receipt — hash-chained history, and a signed receipt naming every item in the batch.
Inter-reader variation stops being a mystery: the record shows where readers agreed, where one was corrected, and by whom. It is the same discipline behind our own compliance and governance work.
Paid remote reading work for radiologists.
Subspecialty reads from wherever your workstation is, with the rate on the job before you take it. Nothing you label enters a patient's care.
The work itself
- Label studies — record each finding, how severe it is, and which side, across the whole study, under a labeling guide.
- Draw segmentations — trace lesions and structures, and measure them on the sequence that matters.
- Grade generated reports — hold a model-written report up against the images and flag what it missed, invented, or mislocated.
- Adjudicate — second-read another radiologist's labels and settle disagreements before delivery.
Jobs come to you when your verified subspecialty fits, and every one states its scope, deadline, and rate. Start at apply as an expert, or read how the program works on expert data annotation jobs.
Identity and credentials are confirmed once, before any job is offered. After that, a peer adjudicates your reads, not a generalist QA team.
- Degree Medical degree and completed radiology residency
- Certification Board certified, or the equivalent specialist registration
- Licence Held now or recently, where you practice
- Experience Years of independent reading
- Fellowship Neuro, MSK, thoracic, breast, or body imaging welcome
You see the rate and the scope, then decide. Approved work is credited and paid out with an itemized record.
- Transparent Rate on every job before you accept
- Remote Your schedule, inside the deadline
- Reviewed Second reader approves, payment releases
- Private Never named on delivered data
Related fields, solutions, and industries.
Radiology AI training data questions.
What is radiology AI training data?
Radiology AI training data is labeled medical imaging that a model learns from or is tested on: findings marked at study and series level, segmentation masks and measurements, reports tagged for negation and laterality, and model-written reports graded against the images. At eeko every label is made by a board-certified radiologist, adjudicated by a second, and delivered with a fingerprint, a consent record, and a signed receipt.
Do you label full DICOM studies or single images?
Both, but we push for full studies. Most labeling mistakes trace back to a missing piece of context: a different series, a different contrast phase, a prior nobody opened. Readers get the whole study and the relevant priors. Deliverables can reference the original series, ship as COCO boxes and masks, or arrive packaged in WebDataset or Parquet, and each batch comes with a signed receipt.
Where does the imaging data come from?
Only from parties who have consented, with that consent attached to each study before it enters a dataset. Studies are fingerprinted on arrival and screened for duplicates, report text is scanned for identifiers and masked, and signed provenance travels with each item. Bring your own images and the labels we add get the same provenance and audit trail.
Is this the same as clinical reporting?
No. You are producing labels and judgments for training and testing software. Nothing you mark enters a patient's chart, replaces a clinical report, or creates a physician-patient relationship. Every job says up front what the labels are for, so you choose with full information, and your name is never attached to delivered data.
Do I need board certification to join?
Most radiology jobs ask for board certification or the equivalent specialist registration, and many specify a subspecialty. A few jobs, for example anatomy labeling or tagging report text, are open to senior residents and fellows. We verify identity and credentials once, before any work is offered, and each job lists what it needs.
How much does radiologist labeling cost?
Pricing is per study or per item and depends on modality, label complexity, and how much adjudication the task needs. Outlining every lesion on a multiphase CT costs more than a study-level classification label. After reading your brief we quote a fixed price for a pilot batch, so you can check the labeling guide and acceptance criteria before scaling. Exclusive licences are priced separately.
Imaging labels a radiologist would sign.
Send the modality, body region, and label schema. A real person replies inside one business day with questions and a pilot price. Radiologists can apply in minutes. No deck. A ranked list.
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