Joan Us

Key Skills

Software

Scale AI

Snorkel AI

Other

Data Annotation Tech

Top Subject Matter

Radiology/Oncology Medical Imaging

Ob/gyn Domain Expertise

Informatics/Machine Learning/NLP Domain Expertise

Top Data Types

Text

Document

Medical Dicom

Top Task Types

Diagnosis

Entity (NER) Classification

Text Generation

Evaluation/Rating

Segmentation

Classification

Text Summarization

Data Collection

Transcription

Computer Programming/Coding

Fine-tuning

Freelancer Overview

Scale AI Radiology Professional (Medical Image Annotation). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Snorkel AI, and Other. Education includes Fellowship, American College of Radiology (2023) and Master of Science, Temple University (2023). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Diagnosis and Entity (NER) Classification.

Labeling Experience

Snorkel AI Clinical Professional (Medical Annotation)

Snorkel AIDiagnosis

Served as a Clinical Professional with Snorkel AI focusing on OB/GYN, Radiology, and Surgery data labeling. Participated in annotation and AI training of medical image datasets to improve clinical decision support systems. Provided subject matter expertise to guide accurate and clinically relevant annotations. • Labeled OB/GYN and surgical medical images for AI models. • Addressed complex radiology cases requiring expert-level annotation. • Trained machine learning models by validating and correcting image-based diagnosis labels. • Enhanced data quality for AI applications in healthcare.

Served as a Clinical Professional with Snorkel AI focusing on OB/GYN, Radiology, and Surgery data labeling. Participated in annotation and AI training of medical image datasets to improve clinical decision support systems. Provided subject matter expertise to guide accurate and clinically relevant annotations. • Labeled OB/GYN and surgical medical images for AI models. • Addressed complex radiology cases requiring expert-level annotation. • Trained machine learning models by validating and correcting image-based diagnosis labels. • Enhanced data quality for AI applications in healthcare.

2024 - Present

Volunteer Physician – Data Terminology Coding (NER Annotation)

OtherTextEntity Ner Classification

Volunteered in Clinical Informatics Health Level 7 Gravity Project with responsibility for terminology coding review. Labeled and classified healthcare concepts using LOINC, SNOMED CT, CPT, and ICD-10 standards to support social determinants of health datasets. Participated in collaborative consensus-building for annotation schema improvements. • Conducted manual NER classification for clinical and public health text. • Used standard terminologies to ensure dataset interoperability. • Reviewed, validated, and refined annotation standards. • Collaborated with other clinical informatics volunteers for quality control.

Volunteered in Clinical Informatics Health Level 7 Gravity Project with responsibility for terminology coding review. Labeled and classified healthcare concepts using LOINC, SNOMED CT, CPT, and ICD-10 standards to support social determinants of health datasets. Participated in collaborative consensus-building for annotation schema improvements. • Conducted manual NER classification for clinical and public health text. • Used standard terminologies to ensure dataset interoperability. • Reviewed, validated, and refined annotation standards. • Collaborated with other clinical informatics volunteers for quality control.

2023 - Present

Scale AI Radiology Professional (Medical Image Annotation)

Scale AIDiagnosis

Worked as a Radiology Professional at Scale AI focusing on reading interpretations, annotations, and report writing for medical images. Labeled and analyzed medical images from multiple specialties such as Cardiothoracic, GI/BODY, Neuro, and MSK Radiology/Oncology. Generated clinical correlations and recommendations based on annotated findings. • Interpreted DICOM medical images for clinical AI training. • Created diagnostic annotations and reports within a structured workflow. • Collaborated with other specialists to refine annotation accuracy. • Used clinical expertise to inform annotation recommendations.

Worked as a Radiology Professional at Scale AI focusing on reading interpretations, annotations, and report writing for medical images. Labeled and analyzed medical images from multiple specialties such as Cardiothoracic, GI/BODY, Neuro, and MSK Radiology/Oncology. Generated clinical correlations and recommendations based on annotated findings. • Interpreted DICOM medical images for clinical AI training. • Created diagnostic annotations and reports within a structured workflow. • Collaborated with other specialists to refine annotation accuracy. • Used clinical expertise to inform annotation recommendations.

2024 - 2024

Biomedical Knowledge Graph Annotation (LLM/NLP Research)

TextEntity Ner Classification

Worked on biomedical knowledge graph development projects using large language models for data annotation. Created and labeled large-scale biomedical text datasets to train entity recognition models. Ensured annotation quality and compliance with representational framework standards. • Labeled biomedical entities in textual datasets for AI training. • Helped refine entity schemas for improved NER accuracy. • Validated and reviewed labeled data for use in machine learning projects. • Supported integration of annotation results into knowledge graph frameworks.

Worked on biomedical knowledge graph development projects using large language models for data annotation. Created and labeled large-scale biomedical text datasets to train entity recognition models. Ensured annotation quality and compliance with representational framework standards. • Labeled biomedical entities in textual datasets for AI training. • Helped refine entity schemas for improved NER accuracy. • Validated and reviewed labeled data for use in machine learning projects. • Supported integration of annotation results into knowledge graph frameworks.

2022 - 2023

Graduate Research – Feature Labeling & Knowledge Graphs

Diagnosis

Performed feature labeling and data annotation for disease prediction and diagnostic models as part of Health Informatics graduate research. Generated biomedical knowledge graphs and utilized genomics and imaging data for feature engineering and AI-driven diagnosis refinement. Built and validated labeled datasets to enable machine learning and deep learning clinical applications. • Annotated features and labels in electronic health records and radiology images. • Developed structured datasets for machine learning model training. • Incorporated genomics and biomarker information into annotated datasets. • Automated precision labeling for risk prediction and diagnostic support.

Performed feature labeling and data annotation for disease prediction and diagnostic models as part of Health Informatics graduate research. Generated biomedical knowledge graphs and utilized genomics and imaging data for feature engineering and AI-driven diagnosis refinement. Built and validated labeled datasets to enable machine learning and deep learning clinical applications. • Annotated features and labels in electronic health records and radiology images. • Developed structured datasets for machine learning model training. • Incorporated genomics and biomarker information into annotated datasets. • Automated precision labeling for risk prediction and diagnostic support.

2021 - 2023

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Education

Work History