Clinical AI training data, built by practicing physicians.
A medical model can ace a board-style quiz and still miss the sepsis hiding in a long history. eeko commissions licensed physicians, nurse practitioners, and physician assistants to write the cases, grade the answers, and check each other's work. Every item carries a fingerprint, a provenance record, and a second clinician's sign-off.
What is clinical AI training data?
Clinical AI training data is everything a medical model learns from and is measured against: a patient case with a reasoned answer, a differential worked through the way an attending would explain it, a model response scored by a clinician, a note with its problems and medications labeled. If that material is thin, scraped, or written by generalists, the model inherits the gaps and will quote the guideline while missing the drug the patient already takes.
General annotators cannot close that gap, because the errors that matter only show up to someone who has stood at the bedside. A confident, well-formatted answer that stops one diagnosis short reads as correct to anyone else. So eeko's data practice sources clinical items from credential-checked clinicians, to your specification, with a second clinician confirming each one.
This page is part of eeko's human-made AI training data practice, which exists because the clinical systems we build for providers and payers (see our healthcare AI work) need data nobody has published. See also training data for healthcare and how eeko verifies human data.
Where medical models go wrong, and what fixes it.
Three failure patterns from clinical model evaluations, and the data decision behind each fix.
Physician annotation and clinical datasets, to your spec.
Six request types we fill most often for clinical teams, each one starting with a spec, then a pilot, at a fixed per-item price.
- Specialty, setting, and acuity to order
- Reference answer and rubric per case
- Ranked differentials, escalation, red flags
- Built for supervised fine-tuning data
- Rubric scores and pairwise rankings
- Feeds RLHF preference data pipelines
- Fingerprinted so contamination is checkable
- See AI evaluation benchmarks
- Entity, relation, and negation labels
- Part of our data annotation services
- Expected refusals and escalations
- Extends red-teaming and safety data
From written spec to signed delivery.
Pilot before volume, fixed price per item, and a clinician on both ends of every item.
Every clinical item, provable.
Medical AI gets audited. The data behind it should survive the audit.
What travels with each item
- Fingerprint — a cryptographic hash taken on arrival and compared across the set for duplicates.
- Signed provenance — who authored it, whether a model proposed any part, and which clinician confirmed it.
- Independent timestamp — proof of when the item existed, from a source we do not control.
- Consent and licence — recorded per item, so you can show what each case may be used for.
- Human-origin checks — generator signatures and C2PA or IPTC fields detected before an item is accepted.
- Audit trail and receipt — hash-chained history, and a signed receipt naming every item in the batch.
It is the same discipline behind our own compliance and governance work.
Paid remote AI work for physicians and clinicians.
Work in your own specialty, from anywhere, with the rate on the job before you take it. No patients, no on-call, nothing published under your name.
The work itself
- Author cases — write realistic presentations, the reasoned answer, and the forks in the road a model has to handle.
- Score model output — grade responses on accuracy, safety, and escalation using a rubric, and note each deduction.
- Label notes — mark problems, medications, findings, and negation in de-identified text, following a written guideline.
- Second-review a peer — confirm or correct another clinician's items before the batch is delivered.
Most clinicians in the network fit this around clinical sessions. Jobs come to you when your verified field 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 reviews your work, not a generalist QA team.
- Degree MD, DO, MBBS, or equivalent
- Licence Held now or recently, where you practice
- Training Residency or specialty training, or equivalent experience
- Practice Years of patient care in the specialty you write for
- NPs and PAs Welcome, with specialty experience
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 clinician approves, payment releases
- Private Never named on delivered data
Related fields, solutions, and industries.
Clinical AI training data questions.
What is clinical AI training data?
Clinical AI training data is the material a medical model learns from and is tested against: patient cases with a reasoned answer, step-by-step diagnostic thinking, model responses scored by a clinician, and labeled clinical notes. Its quality decides whether the model notices a red-flag symptom or misses it. At eeko it is written and reviewed by licensed clinicians working from your written specification, and each item ships with a fingerprint, a provenance record, and the consent or licence that covers it.
How much does physician-written clinical data cost?
Pricing is per item and depends on the specialty, the length and difficulty of each case, and how much second-clinician review the task needs. We quote a fixed price for a pilot batch after reading your brief, so you can check the clinical bar and the format before committing to volume. Exclusive licences and standing monthly orders are priced separately.
Is writing AI training data the same as practicing medicine?
No. You are producing and checking data for software, not caring for anyone. There is no patient, no member of the public receives what you write as advice, and no clinician-patient relationship is created. You bring your clinical judgment to cases, questions, and model outputs under a written specification for each job. Your name is not attached to published data, and you decide which jobs to accept.
Do I need an active medical license to join?
Most clinical jobs ask for a medical degree and a current or recently held license, and we confirm both along with your identity before any work is offered. Many tasks are also open to nurse practitioners and physician assistants who have worked in the relevant specialty. Every listing states what it needs, and we only send you work that matches what we have verified.
How are clinicians paid for AI training work?
Each job states its rate before you accept it, and there is no minimum number of hours. You work remotely, on your own schedule, inside the deadline the job sets. Once a second clinician signs off, the work is credited to you and paid out, with an itemized record of what was approved. Identity and tax details are confirmed once, at onboarding.
Is real patient data used, and how is privacy handled?
Most of what we deliver is authored fresh by clinicians, which means no actual patient is involved. Where notes or tables are sourced, they come only from parties who have consented, that consent is attached to the item, and every document is screened for personal information and masked before an annotator sees it. Each item is fingerprinted and the signed delivery receipt lists every one, so your privacy and clinical safety teams can trace the whole dataset.
Clinical data your safety board can sign off on.
Send the specialty, case mix, and format. A real person replies inside one business day with questions and a pilot price. Clinicians can apply in minutes. No deck. A ranked list.
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Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: