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Demonta Mcbeath

Demonta Mcbeath

Medical Specialist – AI Trainer (Independent Study and Model Evaluation)

USA flagNewton, Usa
$50.00/hrExpertLabel StudioLabelboxCVAT

Key Skills

Software

Label StudioLabel Studio
LabelboxLabelbox
CVATCVAT
Scale AIScale AI
SuperAnnotateSuperAnnotate

Top Subject Matter

Medical diagnosis and clinical reasoning
Legal Services & Contract Review
Regulatory Compliance & Risk Analysis

Top Data Types

TextText
Medical DicomMedical Dicom
ImageImage

Top Task Types

Classification
Entity Ner Classification
Text Generation
Question Answering
Text Summarization
RLHF
Fine Tuning
Red Teaming
Segmentation
Evaluation Rating
Prompt Response Writing SFT
Data Collection

Freelancer Overview

Medical Specialist – AI Trainer (Independent Study and Model Evaluation). Brings 8+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Medicine, Johns Hopkins University School of Medicine (2022) and Bachelor of Science, University of California, Berkeley (2018). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

ExpertFrenchGermanEnglish

Labeling Experience

Medical Specialist – AI Trainer (Independent Study and Model Evaluation)

Text
Contributed clinical expertise for the evaluation of AI diagnostic reasoning in medical scenarios. Reviewed stepwise medical logic and decision-making processes to inform annotation of accurate medical diagnostic paths. Assessed medical literature and cases for evidence-based evaluation to guide AI model output improvements. • Applied clinical reasoning to annotate, review, and rate AI medical outputs. • Focused on the structured analysis of evidence-based clinical data. • Conducted literature review to identify inconsistencies and errors in AI output. • Provided detailed feedback for AI refinement and training.

Contributed clinical expertise for the evaluation of AI diagnostic reasoning in medical scenarios. Reviewed stepwise medical logic and decision-making processes to inform annotation of accurate medical diagnostic paths. Assessed medical literature and cases for evidence-based evaluation to guide AI model output improvements. • Applied clinical reasoning to annotate, review, and rate AI medical outputs. • Focused on the structured analysis of evidence-based clinical data. • Conducted literature review to identify inconsistencies and errors in AI output. • Provided detailed feedback for AI refinement and training.

2022 - Present

Education

J

Johns Hopkins University School of Medicine

Doctor of Medicine, Medicine

Doctor of Medicine
2018 - 2022
U

University of California, Berkeley

Bachelor of Science, Biology

Bachelor of Science
2014 - 2018

Work History

I

Independent Study

Medical Researcher

Newton
2022 - Present
J

Johns Hopkins University

Medical Doctor - Clinical Rotations

Baltimore
2019 - 2022