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S
Shiv

Shiv

Medical AI Tutor System – Response Annotation & Evaluation

USA flagNew York, Usa
$30.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

Clinical Medicine
Medical Q&A
USMLE domains

Top Data Types

TextText

Top Task Types

RLHFRLHF
Question AnsweringQuestion Answering
Data CollectionData Collection

Freelancer Overview

Medical AI Tutor System – Response Annotation & Evaluation. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Bachelor of Medicine, Bachelor of Surgery, Amrita Institute of Medical Sciences (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

IntermediateEnglish

Labeling Experience

Clinical Vignette & Prompt Evaluation – AI Output Review/Labeling

OtherText
Independently reviewed and rated over 1,000 AI-generated clinical explanations during Step 2 CK exam preparation. Focused on identifying subtle errors and unsafe recommendations in LLM-produced answers across multiple specialties. Developed strong intuition for RLHF annotation and model safety evaluation. • Flagged inappropriate drug dosing and contraindication errors consistently. • Critiqued AI medical recommendations for evidence-based accuracy. • Assessed alignment with clinical guidelines in vignette explanations. • Enhanced model safety by careful annotation and review of LLM mistakes.

Independently reviewed and rated over 1,000 AI-generated clinical explanations during Step 2 CK exam preparation. Focused on identifying subtle errors and unsafe recommendations in LLM-produced answers across multiple specialties. Developed strong intuition for RLHF annotation and model safety evaluation. • Flagged inappropriate drug dosing and contraindication errors consistently. • Critiqued AI medical recommendations for evidence-based accuracy. • Assessed alignment with clinical guidelines in vignette explanations. • Enhanced model safety by careful annotation and review of LLM mistakes.

2025 - 2026

Medical AI Tutor System – Response Annotation & Evaluation

OtherText
Engineered and annotated 500+ clinical Q&A pairs for a Socratic AI medical tutoring system using the Claude API. Developed grading rubrics to evaluate reasoning quality, factual rigor, and clarity in AI-generated medical explanations. Applied a detailed error classification framework to annotate and categorize failure modes in LLM output. • Assessed differentials and explanations for evidence alignment and clinical safety. • Flagged misleading, incomplete, or inaccurate clinical content from AI systems. • Used KPRS (Knowledge gap, Process error, Reading error, Silly mistake) categories for labeling errors. • Delivered annotation spanning all major USMLE clinical domains.

Engineered and annotated 500+ clinical Q&A pairs for a Socratic AI medical tutoring system using the Claude API. Developed grading rubrics to evaluate reasoning quality, factual rigor, and clarity in AI-generated medical explanations. Applied a detailed error classification framework to annotate and categorize failure modes in LLM output. • Assessed differentials and explanations for evidence alignment and clinical safety. • Flagged misleading, incomplete, or inaccurate clinical content from AI systems. • Used KPRS (Knowledge gap, Process error, Reading error, Silly mistake) categories for labeling errors. • Delivered annotation spanning all major USMLE clinical domains.

2025 - 2026

Education

A

Amrita Institute of Medical Sciences

Bachelor of Medicine, Bachelor of Surgery, Medicine

Bachelor of Medicine, Bachelor of Surgery
2014 - 2021

Work History

E

Embracing The World

Healthcare Operations & Automation Specialist

New York
2022 - Present