王小白

Key Skills

Software

Other

Top Subject Matter

Medical (Orthopedics/Spine, medical LLM evaluation)

Clinical research data (Spine surgery/orthopedics, vertebral compression fracture, kyphoplasty)

Top Data Types

Text

Top Task Types

Data Collection

Freelancer Overview

Medical AI Trainer & LLM Evaluator (Rubric-based medical response evaluation and rewriting). Core strengths include Other and Microsoft Excel. Education includes Master of Clinical Medicine, Fujian Medical University (2024) and Bachelor of Clinical Medicine, Fujian Medical University (2024). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

Medical AI Trainer & LLM Evaluator (Rubric-based medical response evaluation and rewriting)

OtherText

Evaluated AI-generated medical answers using a structured rubric tailored to clinical safety and reasoning quality. Assessed factual accuracy, clinical logic, completeness of management/differential, red-flag recognition, and whether recommendations appropriately direct users to medical consultation. Rewrote low-quality responses into safer, clearer, and more medically accurate alternatives while checking for hallucinated references and unsupported certainty. • Medical question-answer evaluation and response ranking/rating • Identification of unsafe/overconfident advice, missing differentials, and missing disclaimers • Treatment-summary and clinical reasoning quality review • Benchmark validation via case-based orthopedic and spine scenarios

2024 - Present

Retrospective clinical dataset curator (imaging review, structured variable collection)

Data Collection

Collected and organized retrospective clinical and imaging-related research data to support orthopedic/spine studies. Reviewed preoperative MRI and postoperative X-ray materials and ensured imaging/clinical documentation consistency, missingness handling, and dataset usability for downstream analysis. Built structured data collection sheets for clinical variables, imaging variables, procedural variables, and follow-up outcomes. • Retrospective dataset organization and research-variable definition • Imaging measurement workflow preparation and documentation quality review • Consistency and missing-data checks for research usability • Structured spreadsheet-based clinical/imaging/procedural data collection

2019 - Present

Education

Work History