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OpenTrain AI is seeking a Dermatology Clinical Image AI Reviewer to assess high-resolution images of skin lesions and review AI-derived dermatology assessments. You will help establish accurate, consistent standards for describing and interpreting dermatological images.
The listing identifies this opportunity as entry level, while the required qualifications include a medical degree, an active unrestricted U.S.
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AI training is the human side of building artificial intelligence. Medical specialists help models learn by reviewing examples, evaluating generated outputs, and defining the standards that distinguish accurate, useful responses from unsafe or incomplete ones.
In this role, your dermatology expertise will support image-based evaluation and clinical reasoning for advanced medical AI. The work is non-clinical and does not involve direct patient care.
OpenTrain AI is seeking a Dermatology Clinical Image AI Reviewer to assess high-resolution images of skin lesions and review AI-generated dermatology assessments. You will help establish accurate, consistent standards for describing and interpreting dermatological images.
The listing identifies this opportunity as entry level, while the required qualifications include a medical degree, an active unrestricted U.S. medical license, and at least five years of post-residency clinical dermatology experience.
You will review clinical images of skin lesions, identify dermatological conditions, and describe visible findings using precise medical terminology. You will also assess whether AI-generated dermatology outputs align with clinical reasoning and accepted standards of care.
The role includes developing clear guidance for high-quality image assessments and providing clinical insight that improves model understanding and classification of dermatological presentations. Accuracy, consistency, and adherence to medical and project standards are essential.
This project requires substantial clinical dermatology expertise and the ability to evaluate medical content from images. Candidates must be able to communicate findings clearly in fluent English and apply consistent clinical judgment to AI-generated assessments.
Prior experience with image annotation or medical education is helpful. Familiarity with evaluating clinical content against defined criteria can support accurate and consistent contributions.
AI training and data-labeling work offers specialists a direct way to influence how emerging systems perform in their field. Medical contributors bring essential expertise that general-purpose models cannot reliably provide on their own.
Many AI training projects are flexible and remote, allowing contributors to fit project work around other responsibilities. Specialized knowledge can also open opportunities to contribute to advanced projects at the intersection of professional expertise and technology.