Leopold Bagusat

Key Skills

Software

Label Studio

Appen

Top Subject Matter

Medical Domain Expertise

Healthcare Data Annotation

Clinical Reasoning

Top Data Types

Text

Image

Video

Top Task Types

Diagnosis

Classification

Bounding Box

Segmentation

Object Detection

Text Generation

Freelancer Overview

Freelance Medical AI Evaluator — Outlier AI (Scale AI). Brings 1+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Scale AI, Prolific, and Internal. Education includes Doctor of Medicine, University of Vilnius (2025) and currently working on PhD in Medicine, Medical Faculty Tübingen (2026). AI-training focus includes data types such as Text, Medical, and DICOM and labeling workflows including Evaluation, Rating, and Diagnosis.

Labeling Experience

Doctoral Researcher (Dr. med.) — Medical Faculty Tübingen

Diagnosis

Contributed medical domain annotation for oncology survival prediction models as part of doctoral research work. Performed explainability analysis and worked with class imbalance solutions in AI pipelines handling annotated clinical datasets. Applied expertise in clinical data annotation, oncology AI quality assurance, and survival model evaluation tasks. • Used XGBoost-Cox models for survival prediction in pediatric and young adult melanoma patients • Conducted SHAP-based explainability and internal validation with bootstrapping • Dealt with clinical patient data annotation for AI training • Contributed to oncology-focused AI model quality assurance.

Contributed medical domain annotation for oncology survival prediction models as part of doctoral research work. Performed explainability analysis and worked with class imbalance solutions in AI pipelines handling annotated clinical datasets. Applied expertise in clinical data annotation, oncology AI quality assurance, and survival model evaluation tasks. • Used XGBoost-Cox models for survival prediction in pediatric and young adult melanoma patients • Conducted SHAP-based explainability and internal validation with bootstrapping • Dealt with clinical patient data annotation for AI training • Contributed to oncology-focused AI model quality assurance.

2025 - Present

Medical Expert Participant — Prolific Academic

Text

Participated as a verified medical professional in paid academic research studies and LLM evaluation tasks. Reviewed and assessed LLM output with a healthcare focus for research institutions and AI labs. Contributed to quality assurance by evaluating medical accuracy in AI-generated content. • Involved in medical survey research and assessment of LLM responses • Provided expert medical reviewer feedback • Tasks focused on evaluation rather than annotation creation • Used Prolific platform in an academic research context.

Participated as a verified medical professional in paid academic research studies and LLM evaluation tasks. Reviewed and assessed LLM output with a healthcare focus for research institutions and AI labs. Contributed to quality assurance by evaluating medical accuracy in AI-generated content. • Involved in medical survey research and assessment of LLM responses • Provided expert medical reviewer feedback • Tasks focused on evaluation rather than annotation creation • Used Prolific platform in an academic research context.

2025 - Present

Freelance Medical AI Evaluator — Outlier AI (Scale AI)

Scale AIText

Evaluated large language model (LLM)-generated medical content for clinical accuracy and adherence to standard of care. Provided structured RLHF feedback on medical reasoning tasks to train AI models in frontier pipelines. Performed preference ranking, factual quality assurance review, and error classification across multiple medical subdomains. • Evaluation focused on general medicine, cardiology, oncology, and dermatology domains • Utilized Outlier AI platform as the primary annotation tool • Created RLHF-based structured feedback for LLM training • Tasks included preference ranking and classification for LLM improvement.

Evaluated large language model (LLM)-generated medical content for clinical accuracy and adherence to standard of care. Provided structured RLHF feedback on medical reasoning tasks to train AI models in frontier pipelines. Performed preference ranking, factual quality assurance review, and error classification across multiple medical subdomains. • Evaluation focused on general medicine, cardiology, oncology, and dermatology domains • Utilized Outlier AI platform as the primary annotation tool • Created RLHF-based structured feedback for LLM training • Tasks included preference ranking and classification for LLM improvement.

2025 - Present

Master's-equivalent Thesis — University of Vilnius

Diagnosis

Produced a thesis involving deep learning applications in cardiac MRI analysis and AI-assisted diagnostics. Evaluated model performance using annotated datasets and provided clinical QA for results. Contributed to medical imaging annotation knowledge and skills relevant to cardiology AI QA tasks. • Focused on deep learning for image-based clinical diagnostics • Experience with medical imaging datasets (MRI/CT) • Evaluated model outputs and performed annotation-related QA • Supported improved diagnostic AI through data preparation and review.

Produced a thesis involving deep learning applications in cardiac MRI analysis and AI-assisted diagnostics. Evaluated model performance using annotated datasets and provided clinical QA for results. Contributed to medical imaging annotation knowledge and skills relevant to cardiology AI QA tasks. • Focused on deep learning for image-based clinical diagnostics • Experience with medical imaging datasets (MRI/CT) • Evaluated model outputs and performed annotation-related QA • Supported improved diagnostic AI through data preparation and review.

2025 - 2025

Education

Work History