Physician Reviewer (MD/DO) - Medical AI Evaluation

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based. What this opportunity involves We’re looking for US-based, actively practicing physicians (MD/DO) to evaluate clinical EHR vignettes for a medical AI evaluation program. General and internal medicine are our main focus. While each project involves unique tasks, contributors may: - Evaluate clinical EHR vignettes paired with a question, a proposed answer, and a distractor “trap” answer across diagnosis and treatment tasks, spanning cardiovascular, nervous, hematologic, respiratory, digestive, urinary, reproductive, musculoskeletal, and integumentary systems; - Score the clinical reasoning quality of benchmark items: vignette accuracy and completeness, whether the vignette gives the answer away, answer correctness and gradeability, and trap quality; - Check whether the reasoning chain reaches the answer from vignette facts alone, correct reasoning traces, and write short rationales; - Work within three independent blind reads, followed by physician adjudication. Real clinical complexity only. You’re improving the AI tools you’ll eventually use yourself. If you’re a practicing physician ready to take on this challenging and engaging project, join us! What we look for This opportunity is a good fit for US-based physicians open to part-time, non-permanent projects. Ideally, contributors will have: - Medical degree (MD or DO) and an active, unrestricted US medical license (verified with the issuing medical board); - Board certification or completed residency training, with recent direct patient care (attending-level preferred); - Broad, multi-system diagnostic and treatment experience (internal, family, emergency, or hospital medicine); - Strong clinical reasoning: differential diagnosis, next-step management, and application of evidence-based guidelines; - Prior experience in medical AI evaluation, clinical content or exam-question review, or medical annotation/QA (a plus); - Strong written English (C1+). This opportunity is not fit to medical students, unlicensed medical graduates, non-physician clinicians (NPs, PAs, nurses, pharmacists), or non-clinical healthcare titles. Project time expectations For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted. Compensation Paid per accepted task. Your rate depends on the qualification tier you reach and how efficiently you complete tasks — up to the equivalent of $70/hr. Because payment is per approved task, a faster pace raises your effective hourly rate.

Medical degree (MD or DO) and an active, unrestricted US medical license (verified with the issuing medical board); Board certification or completed residency training, with recent direct patient care (attending-level preferred); Broad, multi-system diagnostic and treatment experience (internal, family, emergency, or hospital medicine); Strong clinical reasoning: differential diagnosis, next-step management, and application of evidence-based guidelines; Prior experience in medical AI evaluation, clinical content or exam-question review, or medical annotation/QA (a plus); Strong written English (C1+).

Evaluate clinical EHR vignettes paired with questions, proposed answers, and distractor answers across multiple medical systems; score clinical reasoning quality of benchmark items (vignette accuracy and completeness, whether the vignette gives the answer away, answer correctness and gradeability, and trap quality); check whether the reasoning chain reaches the answer from vignette facts alone, assess correct reasoning traces, and write short rationales; work within three independent blind reads followed by physician adjudication.