Shevs Connect Institute | SCILabel | Medical AI Annotation Company & Ethical Data Marketplace https://validator.w3.org/feed/docs/rss2.html Data Annotation & Labeling Blog PRACTICE ASSIGNMENT 5 LIVE SESSION 5 RECORDING: Advanced medical image annotation — segmentation, quality review and introduction to SCILabel Polygon annotation technique in CVAT: anchors, curves and precision tips HIPAA in depth: the 18 Safe Harbor identifiers and what counts as PHI, with worked examples EHR systems explained: structure, workflow and where annotators fit in Advanced NER: nested entities, multi-word spans and rare terminology Assertion and negation classification: identifying what is NOT present in clinical notes Named entity recognition (NER) in clinical text: the core schema (diseases, symptoms, medications, anatomy) Clinical text types: SOAP notes, discharge summaries, referral letters and radiology reports — a format guide Healthcare data types: text, images, biosignals and structured records GIGO — garbage in, garbage out: why annotation data quality determines model quality Spotting good vs. poor annotations: a visual field guide to image annotation errors Medical image annotation quality assurance: the six most common rejection categories How machine learning models learn: a non-technical guide for healthcare professionals Why AI is important in healthcare: a brief survey for annotators Semantic vs. instance segmentation: definitions, differences and annotation decisions Advanced object segmentation: polygons vs. bounding boxes — when to use each What is data annotation, and why does it matter? An introduction for new taskers PRACTICE ASSIGNMENT 4 LIVE SESSION 4 RECORDING: Response evaluation & ranking, and biomedical / pharmaceutical AI annotation The global healthcare AI landscape — why clinical annotation is a growth profession Biological pathway and mechanism annotation: KEGG and Reactome reference guide for annotators Home Biomarker classification: diagnostic, prognostic and predictive biomarker annotation Biomedical entity recognition: disease-gene associations, gene/protein mentions and HGVS mutation notation SCI- HEALTHCARE AI TRAINER & DATA ANNOTATION PROGRAM Protocol deviation flagging and ICH-GCP data integrity annotation in clinical trial records Clinical benefit-risk language annotation in pharmaceutical regulatory documents Pharmacovigilance safety signal detection: annotating black box warning triggers and on-label vs. off-label use Clinical trial data annotation: eligibility criteria, endpoints, adverse event classification and CTCAE grading Drug-drug interaction and contraindication annotation Drug dose, route, frequency and indication labelling Adverse drug reaction (ADR) annotation: MedDRA preferred terms, LLT-to-PT mapping and WHO-UMC causality grading Drug and compound annotation: generic names, brand names, drug classes and mechanisms of action AI safety fundamentals for healthcare annotators: identifying harmful and unsafe AI outputs PRACTICE ASSIGNMENT 3 LIVE SESSION 3 RECORDING: Image annotation — tools, modalities, bounding-box annotation and object classification PRACTICE ASSIGNMENT 2 LIVE SESSION 2 RECORDING:RLHF fundamentals, annotator mindset, source verification, guidelines and corrective feedback writing Data & Model Evaluation Our Services footer How It Works header Worked examples: good vs. poor corrective feedback, side by side Corrective feedback writing: the five principles — atomicity, actionability, span-level specificity, tone and comprehensiveness PRACTICE ASSIGNMENT 1 LIVE SESSION 1 RECORDING: Foundations — the healthcare AI landscape, data quality, clinical text and the annotator’s core toolkit