Sudharani Hanumanthu

Key Skills

Software

Google Cloud Vertex AI

Top Subject Matter

No subject matter listed

Top Data Types

Medical Dicom

Top Label Types

Diagnosis

Freelancer Overview

I am a pathologist with over five years of clinical and academic experience, specializing in training and evaluating medical AI systems. My expertise includes medical data annotation, rubric creation for clinical accuracy and safety, prompt engineering, and detailed assessment of AI-generated outputs in pathology, hematology, and laboratory medicine. I have hands-on experience annotating complex pathology datasets, correcting model errors, and providing structured feedback to improve AI reliability and diagnostic reasoning. I am adept at working in remote, asynchronous environments and collaborating with cross-functional teams to ensure high-quality, accurate, and clinically relevant AI training data. My background in digital pathology, deep learning tools, and quality assurance enables me to contribute effectively to AI model development and evaluation in healthtech and medical data domains.

Labeling Experience

Consultant pathologist (AI-assisted pathology annotation and training)

Google Cloud Vertex AIMedical DicomDiagnosis

As a consultant pathologist focusing on AI-assisted pathology, I have been instrumental in annotating medical images and training models for diagnostic tasks. This work includes model correction, prompt creation, rubric development, and refinement to boost ML accuracy in pathology. The core responsibilities revolve around medical data labeling and model improvement for hematology and histopathology. • Labeled and annotated pathology slides for AI diagnostic model training. • Created diagnostic prompts and rubrics for supervised fine-tuning of medical AI. • Performed iterative model corrections and nudging for improved accuracy. • Conducted domain-specific data evaluations to ensure quality and reliability.

As a consultant pathologist focusing on AI-assisted pathology, I have been instrumental in annotating medical images and training models for diagnostic tasks. This work includes model correction, prompt creation, rubric development, and refinement to boost ML accuracy in pathology. The core responsibilities revolve around medical data labeling and model improvement for hematology and histopathology. • Labeled and annotated pathology slides for AI diagnostic model training. • Created diagnostic prompts and rubrics for supervised fine-tuning of medical AI. • Performed iterative model corrections and nudging for improved accuracy. • Conducted domain-specific data evaluations to ensure quality and reliability.

2023

Education

Work History