Andrew Belk

Key Skills

Software

AWS SageMaker

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Data Collection

Evaluation Rating

Question Answering

Text Summarization

Translation Localization

Freelancer Overview

With two years of experience in AI training and a strong background in life sciences, medical research, and emergency medical technology, I have played a key role in developing accurate and reliable AI models. My work has focused on labeling complex medical data, improving AI powered diagnostic tools, and collaborating with teams to enhance model accuracy. I have worked with clinical notes, radiology reports, and biomedical literature, ensuring that data is correctly classified and meets industry standards like HIPAA and FDA guidelines. What sets me apart is my ability to combine medical knowledge with AI training to create high quality data for machine learning models. I have helped develop guidelines for labeling biomedical information, worked on training datasets for predictive analytics, and used my EMT experience to validate AI tools in emergency care. My attention to detail and deep understanding of medical terminology allow me to create accurate and meaningful data that supports innovation in healthcare AI.

Labeling Experience

Anatomy 3

Aws SagemakerAudioAudio Recording

The project focused on creating a high quality dataset to improve AI’s ability to recognize and interpret spoken anatomy terms. It involved labeling and annotating audio recordings of medical terminology, lectures, and discussions related to human anatomy. The goal was to enhance speech recognition models used in medical education, virtual assistants, and diagnostic tools. The main tasks included transcribing audio files, verifying pronunciation accuracy, tagging key anatomical terms, and ensuring clarity in spoken content. Annotators reviewed recordings to confirm correct labeling and alignment with medical standards. The project covered thousands of audio clips, with multiple rounds of quality checks. To ensure accuracy, the team followed strict guidelines, conducted regular validation reviews, and maintained consistency in labeling across all datasets.

The project focused on creating a high quality dataset to improve AI’s ability to recognize and interpret spoken anatomy terms. It involved labeling and annotating audio recordings of medical terminology, lectures, and discussions related to human anatomy. The goal was to enhance speech recognition models used in medical education, virtual assistants, and diagnostic tools. The main tasks included transcribing audio files, verifying pronunciation accuracy, tagging key anatomical terms, and ensuring clarity in spoken content. Annotators reviewed recordings to confirm correct labeling and alignment with medical standards. The project covered thousands of audio clips, with multiple rounds of quality checks. To ensure accuracy, the team followed strict guidelines, conducted regular validation reviews, and maintained consistency in labeling across all datasets.

2024

Biology 2

LabelboxTextQuestion Answering

The project aimed to create a high quality dataset to help AI better understand and answer science related questions. It involved labeling large amounts of text, including research papers, educational materials, and question answer pairs, to improve how AI processes and responds to complex scientific topics. The main tasks included organizing text into categories, highlighting key concepts, and ensuring AI generated answers were accurate and relevant. Annotators reviewed questions and answers to make sure they were clear and aligned with educational standards. The project covered thousands of documents and question answer pairs, with multiple rounds of review. To maintain quality, there were regular accuracy checks, clear guidelines for labeling, and a thorough review process to ensure consistency.

The project aimed to create a high quality dataset to help AI better understand and answer science related questions. It involved labeling large amounts of text, including research papers, educational materials, and question answer pairs, to improve how AI processes and responds to complex scientific topics. The main tasks included organizing text into categories, highlighting key concepts, and ensuring AI generated answers were accurate and relevant. Annotators reviewed questions and answers to make sure they were clear and aligned with educational standards. The project covered thousands of documents and question answer pairs, with multiple rounds of review. To maintain quality, there were regular accuracy checks, clear guidelines for labeling, and a thorough review process to ensure consistency.

2024

Education

Work History