Clinical Dermatology AI Evaluation Expert

You will support clinical data curation and AI model evaluation using a longitudinal dataset of complex and ambiguous dermatology cases. The role combines senior clinical review, consensus resolution, evaluation framework development, expert commentary, and advisory input on model outputs.

You will work with peer faculty reviewers and follow project protocols while documenting clinically defensible decisions. The work includes reviewing graphic medical images and clinically complex cases.

• Lead clinical review of complex or ambiguous dermatology cases.
• Resolve disagreements among reviewers through a structured clinical consensus process.
• Contribute to clinical evaluation frameworks and guideline-aligned case commentary.
• Review AI model outputs and provide expert advisory input.
• Offer subspecialty consultation and document clinical decisions.
• Collaborate reliably with peer faculty reviewers.

What it pays and takes

This is a fully remote, asynchronous independent contractor engagement with occasional synchronous calls. The expected engagement length is four weeks.

• Pay: Compensation is determined by experience, academic standing, and clinical expertise, and is paid per task.
• Schedule: 20+ hours per week.
• Location: Open to candidates in the United States.
• Language: Fluent English.
• Education and appointment: Faculty appointment at the rank of Assistant Professor or above at an accredited US medical school or academic medical center.
• Certification and practice: Board certification in dermatology by the American Board of Dermatology, at least three years of post-residency experience, active clinical practice, and an active medical license in at least one US state.
• Specialty training: Fellowship training in dermatopathology, Mohs or procedural dermatology, pediatric dermatology, or complex medical dermatology.
• Academic background: Peer-reviewed dermatology publications, involvement with AAD, ASDS, or another dermatology specialty society, journal peer review experience, and IRB or research committee service.
• Teaching and communication: Experience teaching residents or fellows, strong written clinical reasoning, familiarity with current dermatology guidelines, and the ability to lead and document clinical consensus.
• Helpful experience: Clinical AI evaluation, digital health, dermatology AI research, clinical evaluation methods, or advising on AI model outputs.

How it works

Apply on OpenTrain with your resume and then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that learn from labeled examples and expert feedback. Medical experts help make clinical data and model evaluations accurate, consistent, and useful for healthcare applications.