Shevs Connect Institute | SCILabel | Medical AI Annotation Company & Ethical Data Marketplace https://validator.w3.org/feed/docs/rss2.html Swedish for Medical Professionals Cohort I-2026 Program Home SCI- HEALTHCARE AI TRAINER & DATA ANNOTATION PROGRAM Clinical AI regulation updates: relevant policy and compliance milestones Advanced source verification: navigating ClinVar, MedDRA, ICD-10 and other authoritative databases Emerging clinical AI areas: new annotation domains added to the SCILabel platform Annotation technology updates: new tools, pre-annotation models and platform changes Client feedback deep dives: analysing real rejection notes and what they teach Understanding task pricing and maximising your earning rate on SCILabel Advanced calibration techniques: building your internal quality compass New client guideline onboarding: how to read a brief you have never seen before Worked examples: good vs. poor corrective feedback, side by side Corrective feedback writing: the five principles (atomicity, actionability, span-level specificity, tone, comprehensiveness) Introduction to red-teaming clinical AI: identifying harmful or unsafe outputs AI safety fundamentals for healthcare annotators: what can go wrong, and why it matters Understanding prompt requirements: what annotators need to know about the prompts behind AI responses Comparative ranking and preference annotation: methodology and justification writing Response evaluation fundamentals: assessing the quality of AI-generated clinical responses What are rubrics? An introduction to evaluation frameworks for AI training Medical image annotation quality assurance: spotting good vs. poor annotations Radiology image annotation: chest X-ray, CT and MRI labeling fundamentals Digital pathology image annotation: whole slide imaging and tissue labeling Cardiology image annotation: echocardiograms, angiograms and ECG-linked imaging Dental and oral radiology annotation: OPG, CBCT and common findings Wound care image annotation: a worked field guide Advanced object segmentation: polygons vs. boxes, and when to use each Object classification in medical images: assigning the correct label class Box annotation fundamentals: drawing accurate, tight bounding boxes DICOM format, metadata and medical image viewers: RadiAnt, Weasis and OsiriX setup guide Medical imaging modalities: X-rays, CT, MRI, ultrasound, PET and endoscopy — a visual annotator’s guide Introduction to annotation tools: Label Studio, CVAT and Labelbox — a visual guide The adaptability mindset: why every project guideline is different, even within the same family How to read and apply a project annotation guideline Practical guide Assessing source weight: publisher credibility, recency and study quality Finding reliable sources: the hierarchy of evidence for medical annotators What makes a good medical annotator? The mindset behind reliable annotation work RLHF fundamentals: how human feedback trains clinical AI models 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 How machine learning models learn: a non-technical guide for healthcare professionals Why AI is important in healthcare: a brief survey for annotators What is data annotation, and why does it matter? An introduction for new taskers The global healthcare AI landscape — why clinical annotation is a growth profession Blog