Senior Dermatology Clinical AI Reviewer

You will provide expert clinical judgment on complex and ambiguous dermatology cases for AI evaluation and data curation. The work includes reviewing medical images, helping resolve disagreements, and supporting a longitudinal dermatology dataset.

You will also help improve how AI models are assessed and document clinically sound, guideline-aligned reasoning. The role includes consultation when a case needs subspecialty expertise.

• Lead clinical review of complex or ambiguous dermatology cases.
• Facilitate structured resolution when reviewers disagree.
• Contribute to clinical evaluation frameworks for AI model assessment.
• Advise on reviews of AI model outputs.
• Write clinically defensible case commentary aligned with current guidelines.
• Document consensus and provide subspecialty consultation when appropriate.

This is a fully remote, asynchronous independent contractor engagement. The project is expected to last four weeks and averages 15 hours per week, with up to 40 hours during peak batches.

The work requires senior clinical experience, strong written reasoning, careful protocol use, and the ability to lead consensus on complex medical images.

• Paid per task; compensation is based on experience, academic standing, and clinical expertise.
• Open to candidates in the United States.
• Professional fluency in English is required.
• Faculty appointment at the Assistant Professor level or above at an accredited US medical school or academic medical center.
• Board certification in dermatology by the American Board of Dermatology.
• At least three years of experience after completing residency.
• Active clinical practice and a medical license in good standing in at least one US state.
• Strong written articulation of clinical reasoning and familiarity with current dermatology guidelines.
• Disciplined adherence to protocols and the ability to lead and document clinical consensus.
• Helpful preparation includes fellowship training in dermatopathology, Mohs or procedural dermatology, pediatric dermatology, or complex medical dermatology.
• Peer-reviewed dermatology publications, journal peer review, research committee service, resident or fellow teaching, specialty-society involvement, or prior clinical AI evaluation experience are also relevant.

AI training is the human work behind systems that learn from examples, reviews, and expert feedback. Medical experts help make these systems more accurate by judging outputs, explaining clinical reasoning, and preparing reliable data for evaluation.