Anthony Pera

Key Skills

Software

LabelImg

Label Studio

Scale AI

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Document

Image

Text

Video

Top Label Types

Question Answering

Diagnosis

RLHF

Evaluation Rating

Red Teaming

Freelancer Overview

I am a dental sciences professional with hands-on experience in AI model evaluation and training data annotation, particularly within the medical and clinical domains. My background includes reviewing and ranking AI-generated clinical responses for accuracy, logical consistency, and safety, as well as identifying errors and providing structured feedback to improve model performance. I have developed case-based diagnostic questions to enhance AI reasoning and applied evidence-based standards to ensure high-quality data labeling. My skills in clinical data analysis, critical reasoning, and research synthesis enable me to deliver precise, reliable annotations and evaluations, making me adept at supporting AI projects that require medical expertise and analytical rigor. I am comfortable working independently in remote, detail-oriented environments and am committed to advancing the quality of AI-driven healthcare solutions.

Labeling Experience

Clinical AI Response Evaluation & Medical Reasoning Annotation

LabelimgTextQuestion AnsweringDiagnosis

This project involved evaluating and annotating AI-generated medical and dental responses to improve clinical reasoning accuracy, safety, and alignment with evidence-based guidelines. Responsibilities included reviewing diagnostic outputs, ranking responses using structured rubrics, identifying hallucinations and unsafe treatment recommendations, and providing corrective feedback for supervised fine-tuning and reinforcement learning from human feedback (RLHF) workflows.

This project involved evaluating and annotating AI-generated medical and dental responses to improve clinical reasoning accuracy, safety, and alignment with evidence-based guidelines. Responsibilities included reviewing diagnostic outputs, ranking responses using structured rubrics, identifying hallucinations and unsafe treatment recommendations, and providing corrective feedback for supervised fine-tuning and reinforcement learning from human feedback (RLHF) workflows.

2022 - 2025

Education

Work History