SCI HEALTHCARE AI ANNOTATION & DATA LABELLING TRAINING WEBINAR SERIES
Every Wednesday | July & August 2026 3rd July – 28th August 2026 | 9 Sessions Ksh. 800 Per Session | 3 Hours Per Session
What You Gain
Programme Benefits
Here's what you'll gain from this comprehensive training series
Acquire healthcare domain expert annotation and labelling skills — master the techniques that global AI companies demand.
Opportunity to enrol in further FREE training on our website to become proficient in all 5 specialist tracks.
Opportunity to be onboarded onto the SCILabel platform to begin accessing paid remote annotation tasks from global clients.
Earn a Certificate of Proficiency upon successful completion of all sessions — recognised by healthcare AI employers.
Learn from globally recognised healthcare AI annotation and evaluation experts (Keynote Sessions 2 & 5) — gain insights from world-class practitioners.
Network with a growing community of African healthcare AI professionals — connect, collaborate, and grow your career.
9 Sessions
Full Session Schedule & Topics
Progress from introductory concepts through to advanced specialist skills — ensuring all healthcare professionals, regardless of prior AI experience, gain actionable, marketable annotation competencies.
Session 1
Beginner
Friday, 3rd July 2026
3 Hours • SCI Lead Instructor
Introduction to Healthcare AI & Data Annotation
- What is Healthcare AI? Real-world clinical AI applications in 2026
- Why healthcare professionals are the most valuable AI annotators
- Overview of data annotation, labeling, and model training workflows
- Your career pathway: from annotation novice to certified SCILabel tasker
- Introduction to annotation tools: Label Studio, CVAT, Labelbox
Session 2
Beginner–Intermediate
Friday, 10th July 2026
3 Hours • SCI Lead Instructor
Medical Text Annotation & Clinical NLP Fundamentals
- Clinical NLP: how AI reads and understands medical text
- Named Entity Recognition (NER) in EHRs, SOAP notes & discharge summaries
- Annotating diseases, symptoms, medications, dosages & procedures
- PHI de-identification: HIPAA, GDPR & ethical standards
- Hands-on exercise: annotating a synthetic clinical text dataset
⭐ Keynote Speaker 1: Global Healthcare AI NLP Expert — The Future of Clinical NLP & Why Annotator Quality Wins
Session 3
Intermediate
Friday, 17th July 2026
3 Hours • SCI Lead Instructor
Medical Image Annotation & Computer Vision in Healthcare
- How AI 'sees' medical images: radiology, pathology & ophthalmology AI
- Bounding boxes, segmentation masks & polygon annotation techniques
- Annotating chest X-rays, CT slices, MRI scans & pathology slides
- DICOM format, image viewers & annotation tool environments
- Quality standards for medical image labeling projects
Session 4
Intermediate
Friday, 24th July 2026
3 Hours • SCI Lead Instructor
Clinical Coding, ICD-10 & Adverse Event Annotation
- ICD-10, SNOMED CT, LOINC & CPT: mapping clinical findings to codes
- Adverse drug event & pharmacovigilance text annotation (MedDRA)
- Clinical relation extraction: drug-disease, treatment-outcome links
- Assertion & negation detection in clinical notes
- Annotating clinical trial and research protocol documents
Session 5
Intermediate–Advanced
Friday, 31st July 2026
3 Hours • SCI Lead Instructor
RLHF & Clinical AI Evaluation — Assessing AI Responses
- What is RLHF (Reinforcement Learning from Human Feedback) in healthcare?
- Evaluating AI-generated clinical responses for accuracy & safety
- Preference ranking, red-teaming & clinical safety scoring
- Evaluating AI diagnostics, treatment suggestions & patient communication
- Real-world RLHF tasks on SCILabel: what to expect & how to earn
⭐ Keynote Speaker 2: Global AI Evaluation & RLHF Domain Expert — Human Judgment in the Age of Clinical AI — Why Expert Evaluators Are the Last Line of Defense
Session 6
Intermediate
Friday, 7th August 2026
3 Hours • SCI Lead Instructor
AI Ethics, Data Privacy & Regulatory Compliance for Annotators
- AI ethics in healthcare: bias, fairness & annotator responsibility
- HIPAA, GDPR, Kenya Data Protection Act 2019 & WHO AI Ethics Framework
- EU AI Act 2024 & FDA SaMD compliance for clinical AI annotators
- Annotation bias: how your decisions shape AI behaviour
- Professional integrity, confidentiality & ethical escalation
Session 7
Advanced
Friday, 14th August 2026
3 Hours • SCI Lead Instructor
Genomics, Biomedical Data & Specialist Annotation
- Genomic data types: DNA sequencing, variant calling & biomarker annotation
- Annotating clinical research datasets, lab results & biosignals
- Specialist annotation for biotechnology & pharmaceutical AI clients
- Social Determinants of Health (SDOH) annotation schema
- Building your specialist annotator profile for high-value tasks
Session 8
Intermediate–Advanced
Friday, 21st August 2026
3 Hours • SCI Lead Instructor
Annotation Quality, Inter-Rater Agreement & QA Standards
- Quality assurance frameworks for AI annotation projects
- Cohen's Kappa, inter-rater agreement & calibration sessions
- Common annotation errors & how to self-audit your work
- Client acceptance criteria & professional QA reporting
- Annotation style guides: decision trees, edge cases & worked examples
Session 9
All Levels
Friday, 28th August 2026
3 Hours • SCI Lead Instructor + Special Guest
SCILabel Platform Onboarding & Launching Your Annotation Career
- SCILabel platform walkthrough: task queues, payment, and project management
- How to access paid remote annotation tasks from global clients
- Building your annotation portfolio and passing quality benchmarks
- Career pathways: from tasker to senior annotator to project lead
- Enrolment to SCI's full Healthcare AI Trainer & Data Annotation Program
Don't miss out — secure your spot in these transformative sessions.
Explore All SessionsHow SCILabel Serves This Industry
Data Collection
We source dental radiograph datasets — panoramic (OPG), periapical, bitewing, and CBCT — from dental clinics, dental schools, and oral health research centres through direct purchase and revenue-sharing agreements. All data is de-identified.
Data Annotation & Labeling
Our dental-trained annotators label caries (per tooth, per surface, per severity), alveolar bone levels for periodontal AI, root morphology, impacted tooth position (Winter's classification), crown and restoration status, and periapical lesion detection. We support standard dental ontologies including FDI and Universal numbering systems.
Data & Model Evaluation
Dental evaluators benchmark caries detection AI against dentist ground truth, assess sensitivity on early (enamel) caries versus advanced lesions, and test for performance variation across radiograph quality and imaging system.
Annotation Types & Formats
- Tooth-level bounding box and polygon annotation on OPG
- Caries classification per surface and severity (ICDAS)
- Alveolar bone level measurement for periodontal AI
- Periapical lesion detection and segmentation
- Impacted third molar classification (Winter's / Pell-Gregory)
Specialist Workforce Tracks
Track 2 (Medical Imaging): with dental specialisation: Dentists, Dental Therapists, Dental Radiographers.
Example Deliverable
Client Output Example
A 3,000-image annotated OPG dataset with per‑tooth caries labels, bone level measurements on posterior teeth, and impacted third molar classifications — delivered with QA certification and annotator concordance report.