Suhas K.

Key Skills

Software

No software listed

Top Subject Matter

Medical/clinical case data (diagnostics, histories, lab/imaging-derived findings)

Medical text annotation and clinical case documentation

Healthcare query answering and medical response evaluation

Top Data Types

Text

Top Task Types

Entity (NER) Classification

Question Answering

Freelancer Overview

Clinical training medical case evaluation with medical label quality assurance. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N and A. Education includes Bachelor of Medicine and Bachelor of Surgery, Orenburg State Medical University (2026). AI-training focus includes data types such as Medical, DICOM, and Text and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Medical LLM response evaluation and fact-checking for healthcare Q&A

TextQuestion Answering

I performed LLM/AI response review for healthcare queries by applying medical expertise to identify what is correct, incomplete, or inaccurate. I focused on fact-checking and clinical plausibility so that AI-generated answers can be refined through evaluation. This corresponds to training/evaluation workflows used in QA and medical response scoring. • Review and critique of healthcare Q&A style outputs • Fact-checking medical AI outputs against clinical knowledge • Identifying medically inaccurate or unsafe content • Providing expert-grade evaluation signals for improvement

2020 - Present

Medical text annotation via clinical history writing and structured case documentation

TextEntity (NER) Classification

Throughout clinical training, I wrote clinical histories and participated in diagnosis and management planning, producing structured medical text that can serve as labeled documentation. I worked with medically grounded terminology and clinical reasoning to capture key patient entities and clinical attributes in a consistent format. This experience maps to text-based data labeling tasks such as entity labeling and clinical case documentation labeling. • Clinical history writing with structured medical terminology • Extraction/implicit labeling of patient and clinical entities • Consistency checks for medically grounded descriptions • Clinical reasoning informed classification of case elements

2020 - Present

Clinical training medical case evaluation with medical label quality assurance

During clinical training, I evaluated real patient cases by interpreting clinical findings and diagnostic signals, which aligns with quality assessment for medical labels. I reviewed and validated medical histories, lab results, ECGs, and imaging/X-ray information to ensure medically accurate case information. This evidence-based review supports the reliable labeling signals needed for training and validating healthcare AI outputs. • Clinical case evaluation and label validation • Interpretation of lab reports, ECGs, and imaging findings • Evidence-based filtering of medically accurate vs. inaccurate information • Support for diagnostic accuracy scoring and QA-style checks

2020 - Present

Education

Work History