David Istrager

Key Skills

Software

CrowdSource

Top Subject Matter

No subject matter listed

Top Data Types

Medical Dicom

Top Label Types

Diagnosis

Translation Localization

Land Cover Classification

Transcription

Freelancer Overview

I am a board-certified medical oncologist and physician-scientist with over 12 years of experience in clinical oncology, precision medicine, and clinical trial development. My expertise includes structured clinical data annotation, evaluation of AI-assisted oncology workflows, and creating clinical rubrics for AI validation in healthcare. I have hands-on experience integrating AI-driven decision support tools into oncology practice, conducting eligibility assessments for clinical trials, and annotating complex oncology cases for trial matching. My background also includes translational research in immunotherapy and biomarker-driven therapy, as well as reviewing and validating agentic AI systems for oncology use cases. I am passionate about improving the accuracy and utility of medical data for AI training and clinical applications.

Labeling Experience

Clinical Data Expert, Structured Clinical Annotation for AI Oncology Workflows

CrowdsourceMedical DicomDiagnosisTranslation Localization

Structured clinical annotation was performed for oncology patient data supporting AI-assisted clinical trial matching. I evaluated AI-based oncology decision workflows and eligibility assessments for precision medicine applications. Data reviewed included molecular profiles, clinical documentation, and eligibility criteria within oncology trials. • Annotated clinical and molecular data for use in AI clinical decision support tools. • Validated AI outputs against established clinical eligibility guidelines. • Ensured annotation quality for downstream clinical trial matching algorithms. • Facilitated multidisciplinary review of annotated cases.

Structured clinical annotation was performed for oncology patient data supporting AI-assisted clinical trial matching. I evaluated AI-based oncology decision workflows and eligibility assessments for precision medicine applications. Data reviewed included molecular profiles, clinical documentation, and eligibility criteria within oncology trials. • Annotated clinical and molecular data for use in AI clinical decision support tools. • Validated AI outputs against established clinical eligibility guidelines. • Ensured annotation quality for downstream clinical trial matching algorithms. • Facilitated multidisciplinary review of annotated cases.

2020

Education

Work History