Michael Bell

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Geospatial Tiled Imagery

Image

Top Label Types

Classification

Evaluation Rating

Question Answering

Text Generation

Tracking

Freelancer Overview

With over 14 years of clinical experience as a neurosurgeon, psychiatrist, and pathologist, I specialize in bridging the gap between frontline medicine and digital innovation. My expertise lies in data labeling and annotation for AI training, particularly within complex medical domains. I have hands-on experience developing high-fidelity clinical scenarios, categorizing medical data for machine learning, and auditing AI-generated outputs for accuracy, logical coherence, and bias. I am skilled at translating nuanced clinical decision points into structured, actionable data sets, ensuring AI models are both safe and clinically relevant. My background also includes proficiency with EHR systems, medical research databases, and collaborative tools, as well as a strong commitment to ethical AI deployment in healthcare.

Labeling Experience

Segmentation for Urban Autonomous Navigation

Scale AIImageClassificationQuestion Answering

Provided high-precision pixel-level masks for 20+ distinct classes, including drivable surfaces, pedestrians, lane markings, and complex occlusions in high-density urban environments. Annotated over 4,500 high-resolution frames with strict adherence to "zero-gap" topology rules. Handled edge-case scenarios such as motion blur, low-light conditions, and overlapping objects. Maintained a consistent 98.5% Quality Score. Acted as a "Gold Standard" reviewer for junior annotators, resolving consensus conflicts and performing root-cause analysis on recurring labeling errors to update the project style guide.

Provided high-precision pixel-level masks for 20+ distinct classes, including drivable surfaces, pedestrians, lane markings, and complex occlusions in high-density urban environments. Annotated over 4,500 high-resolution frames with strict adherence to "zero-gap" topology rules. Handled edge-case scenarios such as motion blur, low-light conditions, and overlapping objects. Maintained a consistent 98.5% Quality Score. Acted as a "Gold Standard" reviewer for junior annotators, resolving consensus conflicts and performing root-cause analysis on recurring labeling errors to update the project style guide.

2024 - 2025

Education

Work History