AI Data Labeler or Annotator
I contributed to a medical AI training data project focused on developing accurate datasets for healthcare and clinical AI applications. Project Scope: The project involved high-precision annotation of medical and health-related data to support the training of machine learning models used in clinical decision support, disease detection, and patient record analysis. Key Tasks Performed: - Accurately labeled and annotated medical text, clinical notes, radiology reports, or medical images (e.g., X-rays, CT scans, or MRI) according to strict medical annotation guidelines - Performed tasks such as medical entity recognition (e.g., symptoms, diagnoses, medications, procedures), classification of medical conditions, or bounding box annotation for abnormalities in medical imaging - Identified complex cases involving ambiguous medical terminology, rare conditions, or overlapping symptoms, and applied domain-specific guidelines - Ensured compliance with data privacy and quality standards relevant to healthcare datasets Project Size: Labeled and annotated several thousand medical data samples (text and/or images) between July and December 2025. Quality Measures Adhered To: - Maintained high accuracy and inter-annotator agreement by strictly following medical annotation protocols and clinician-reviewed guidelines - Conducted thorough self-review and double-checking of annotations to minimize errors in sensitive health data - Worked closely with feedback loops from senior reviewers and medical experts to continuously improve labeling quality and consistency This experience deepened my understanding of medical terminology, clinical workflows, and the critical importance of precision and reliability when creating training data for healthcare AI systems.