Physician AI Trainers — MDs and DOs for Medical AI Evaluation

PhysicianRecruitment.com staffs board-certified MDs and DOs for healthcare-AI training, clinical reasoning evaluation, and medical model red-teaming. Frontier AI labs and clinical-LLM companies — including Hippocratic AI, OpenEvidence, OpenAI, Anthropic, Scale AI, Surge, Mercor, and Centaur AI — increasingly require licensed physicians to evaluate model outputs on differential diagnosis, pharmacology, clinical reasoning, and medical safety. Crowd-platform annotators cannot replicate the legal, clinical, and pharmacologic judgment a practicing physician brings to medical-AI evaluation.

We recruit physicians exclusively. Every trainer in our network is an MD or DO with a verified state license, an active board certification, and a documented clinical practice. Most accept fully remote, asynchronous engagements alongside continued patient care. Read more about the clinical-AI landscape at NEJM AI and the AMA's framework on augmented intelligence in healthcare.

Why Physician-Trained AI Outperforms Generic Models

Medical reasoning is not a generic-language task. A physician evaluating an AI-generated differential weighs prevalence, pretest probability, age and sex distributions, drug interactions, contraindications, red-flag symptoms, malpractice exposure, and the standard of care expected from a board-certified clinician in that jurisdiction. None of those judgments transfer cleanly from a non-clinical annotator, and none are reliably encoded in textbook training data alone.

Medical liability awareness is the second irreplaceable layer. Every clinical recommendation a model produces is a potential medico-legal artifact. Physician evaluators apply the same risk calculus to model output that they apply to their own charts: would a peer reviewer, a malpractice carrier, or a state medical board defend this recommendation? That question cannot be crowd-sourced. It is the daily judgment of a licensed clinician.

Regulatory context matters too. The FDA's Software as a Medical Device (SaMD) framework treats clinical decision support that drives diagnosis or treatment as a regulated device. Models that touch SaMD territory require evaluation by clinically qualified reviewers with an understanding of intended use, risk classification, and post-market surveillance — exactly the framework practicing physicians work in every day.

Physician AI Use Cases We Staff

RLHF for medical large language models — physician preference rankings on paired model outputs covering diagnosis, treatment, patient education, and clinical documentation.
Clinical reasoning benchmark construction — authoring and validating multi-step diagnostic vignettes (USMLE-style, NBME-style, board-review-style) for evaluation harnesses.
Red-team medical safety evaluation — adversarial prompting to surface harmful, off-label, contraindicated, or out-of-scope medical advice; jailbreak resistance for clinical guardrails.
Diagnostic accuracy evaluation — scoring model differentials against gold-standard physician panels, including subspecialty-specific accuracy on rare presentations.
Pharmacology and drug-interaction review — verifying dosing, renal/hepatic adjustments, pediatric dosing, pregnancy categories, and interaction matrices generated by AI systems.
Clinical documentation evaluation — review of AI-generated SOAP notes, H&Ps, discharge summaries, and ambient-scribe output for accuracy, completeness, and billing-compliance risk.
Medical literature synthesis review — verifying citations, evidence grading, and recommendation alignment with current guidelines (USPSTF, AHA, ACS, IDSA, etc.).
Patient-facing safety review — evaluating AI-generated patient education, triage advice, and symptom-checker output for appropriate safety-netting and escalation.

Physician Specialties Available for AI Training

Our physician network spans every active recruiting specialty. AI-training engagements are available across:

Primary care: Family Medicine, Internal Medicine, Pediatrics, OB/GYN — broad-spectrum reviewers ideal for general clinical-LLM evaluation.
Acute care: Emergency Medicine, Hospitalist Medicine, Critical Care, Anesthesiology — high-acuity reasoning, triage, and protocol evaluation.
Mental health: Psychiatry (adult, child & adolescent, addiction, geriatric), Psychology consult — safety-critical for crisis, suicidality, and medication evaluation.
Medical subspecialties: Cardiology, Endocrinology, Gastroenterology, Pulmonology, Nephrology, Rheumatology, Infectious Disease, Hematology/Oncology, Neurology, Dermatology.
Surgical specialties: General Surgery, Orthopedic Surgery, Neurosurgery, Cardiothoracic, Vascular, Urology, Otolaryngology, Plastic, Colorectal, Pediatric Surgery.
Diagnostic specialties: Radiology (diagnostic, interventional, neuroradiology), Pathology — essential for image-AI and digital-pathology evaluation.
Other: PM&R, Pain Management, Sports Medicine, Geriatrics, Palliative Care, Occupational Medicine, Preventive Medicine, Medical Genetics.

Engagement Models

We structure physician-AI engagements to match how AI teams actually work and how physicians actually have time:

Asynchronous per-task — physicians complete discrete evaluation tasks on their own schedule. Best for benchmark scoring, RLHF preference labeling, and bulk red-team prompting. Typical commitment: 5-20 tasks per week.
Hourly contract (1099) — physicians commit a defined hours-per-week block (commonly 5-15 hours) at an agreed hourly rate. Best for ongoing evaluation programs and longitudinal red-team campaigns.
Project-based — fixed-scope engagements with a defined deliverable (benchmark dataset, evaluation report, safety audit). Best for one-time launches and regulatory submissions.
Retainer — monthly retainer for on-call physician advisory across multiple model releases. Best for AI labs running continuous model updates and needing fast clinical sign-off.
Hybrid — combinations of the above, including async per-task with a guaranteed minimum, or hourly contract with project bonuses tied to throughput milestones.

Why Licensed Physicians Over Crowd Platforms

Crowd-platform medical annotators are typically untrained or minimally credentialed reviewers — pre-med students, nursing students, or general crowd workers with self-reported medical background. The accuracy gap on clinical-reasoning tasks is not a small one. A licensed physician brings four credentialing layers a crowd worker cannot:

Verified state medical licenses — every physician in our network has at least one active, unrestricted state license that we verify against the state medical board's primary source.
Active board certifications — verified through ABMS or AOA primary source. Board status is current, not lapsed.
CME-current — physicians in active practice maintain continuing medical education in their specialty, keeping evaluation judgment aligned with current standards of care.
HIPAA-aware and malpractice-context trained — practicing physicians work daily inside HIPAA, informed-consent, and malpractice frameworks. They flag PHI, off-label recommendations, and standard-of-care deviations instinctively in a way crowd workers cannot.

For AI systems intended for clinical deployment, the credentialing of the evaluator is part of the regulatory and liability story. A model evaluated by board-certified physicians has a defensible evaluation provenance. A model evaluated by crowd workers does not.

Our Process

Discovery (Days 1-3): 30-minute scoping call with your AI/ML lead, clinical lead, or program manager. We define the use case, the specialty mix needed, the volume, the rate, and the timeline.
Credentialed matching (Days 3-10): We surface 5-15 verified physician candidates per requested specialty from our network. Each profile includes specialty, board status, license states, AI-training experience to date, and target weekly hours. We verify all credentials before introduction.
Contract and onboarding (Days 7-14): We coordinate 1099 master service agreements, NDAs, IP assignment, payment terms, and platform access. Physicians can start evaluation work within 1-2 weeks of contract signature.
Quality review (Ongoing): We run a quarterly check-in on physician throughput, employer satisfaction, and pipeline expansion. Replacement physicians are sourced within 5-10 business days if a placement is not the right fit.

Ready to Recruit Physicians for Your AI Project?

Email hire@physicianrecruitment.com with your use case, the physician specialties needed, your target weekly hours, and your timeline. We respond within one business day with an initial roster of verified candidates and proposed engagement structure. There is no fee to receive the initial roster — fees apply only on successful placement.

FAQ

What physician credentials do you require?

Every physician in our network is an MD or DO with at least one active, unrestricted state medical license verified against the state medical board's primary source, plus an active board certification verified through ABMS or AOA. Most are in active clinical practice. Subspecialty fellowship training is documented per engagement.

How are credentials verified?

State licensure is verified through the state medical board primary source. Board certification is verified through ABMS or AOA primary source. Malpractice history is reviewed through NPDB queries on engagements where employers require it. DEA registration is verified separately when the engagement involves controlled-substance evaluation.

Can physicians work asynchronously around clinical schedules?

Yes — asynchronous, evening, weekend, and post-call evaluation work is the most common engagement structure. Most physicians in our network maintain primary clinical practice and treat AI training as 5-15 hours of supplementary income per week.

What does compensation typically look like?

Compensation varies by specialty, engagement type, and complexity. Hourly rates for general-medicine RLHF and evaluation typically run $75-$150 per hour. Subspecialty evaluation (radiology, pathology, surgical specialties) and red-team safety work typically runs $150-$300 per hour. Project and retainer rates are negotiated per engagement.

What is the typical starting timeline?

Initial physician roster delivered within 3-7 business days of the discovery call. Contracted physicians can begin evaluation work within 1-2 weeks of contract signature. Larger or specialty-specific engagements may take 2-4 weeks to fully staff.

How do you handle HIPAA and PHI?

Physician evaluators sign Business Associate Agreements when an engagement involves real PHI. Most AI evaluation engagements use de-identified or synthetic data, which removes the BAA requirement. We coordinate the appropriate agreements with your legal and compliance team before access is granted.

Which physician specialties are most available?

Family Medicine, Internal Medicine, Emergency Medicine, Hospitalist Medicine, Psychiatry, and Pediatrics are the highest-volume specialties. Radiology, Pathology, and surgical subspecialties are available with slightly longer roster timelines (1-3 weeks). Every specialty in active US practice is recruitable on request.

Can physicians scale up over time?

Yes. Most engagements grow from a small pilot (5-10 physicians, 5-10 hours each per week) to a steady-state program (25-100 physicians, varied hours). We add specialty coverage and physician headcount on the cadence your program needs.

Physician? Apply to Our AI Talent Pool.

Practicing MDs and DOs interested in part-time, asynchronous AI training and evaluation work can apply to our AI talent pool. Most engagements are fully remote, evening or weekend friendly, and structured around your clinical schedule. We currently have active demand across primary care, psychiatry, emergency medicine, hospitalist medicine, radiology, and pathology — but accept physicians from all specialties for ongoing pipeline development.