The Shevs Connect Institute (SCI) Healthcare AI Trainer & Data Annotation Program is an advanced professional training program designed to prepare healthcare, clinical research, and life sciences professionals for the rapidly growing global healthcare artificial intelligence industry.
Powered by the SCILabel Platform, this comprehensive 5-track curriculum equips learners with practical, industry-relevant skills in healthcare AI data annotation, AI model evaluation, medical image labeling, genomics intelligence, and AI safety compliance. The program combines healthcare domain expertise with real-world AI workflows, enabling participants to transition into globally competitive remote AI project opportunities.
Unlike general AI annotation courses, this program is specifically tailored for healthcare professionals whose clinical knowledge allows them to interpret complex medical data with the precision and scientific understanding required by international healthcare AI companies, pharmaceutical firms, biotech organizations, medical device manufacturers, and enterprise AI labs.
The curriculum consists of 109 structured lessons and 109 short but detailed practical assignments delivered through Microsoft Teams and the SCI Canva LMS over a 6–8 week learning pathway. Every practical assignment mirrors real billable work conducted on the SCILabel platform, allowing learners to build a verified professional portfolio while training.
The program is divided into five specialized tracks:
- Medical Text Annotation & Natural Language Processing (NLP)
- Medical Image Labeling & Computer Vision
- RLHF — Clinical AI Response Evaluation
- Genomics & Biomedical Data Intelligence
- AI Safety, Ethics & Regulatory Compliance
Learners gain hands-on experience with clinical datasets, annotation platforms, AI evaluation frameworks, and healthcare regulatory standards while developing skills highly sought after in the international AI workforce.
Program Process
The SCI Healthcare AI Trainer & Data Annotation Program follows a structured professional development pathway designed to transition learners from training into live remote AI work opportunities through the SCILabel platform.
The process includes:
- Enrollment & Registration
Participants complete enrollment and gain access to the SCI learning systems, Microsoft Teams classroom environment, and SCI Canva LMS platform. - 6–8 Week Professional Training Program
Learners undergo intensive training covering all five specialized healthcare AI tracks through structured lessons, practical assignments, workshops, and simulated industry projects. - Instructor Evaluation Session
Upon completion of training, participants undergo a 1-hour professional evaluation conducted by SCI instructors to assess annotation quality, AI evaluation skills, clinical reasoning accuracy, and readiness for live projects. - Onboarding & SCILabel Registration
Successful participants are onboarded onto the SCILabel platform, where they receive a verified Tasker ID and professional profile setup. - Task Acceptance & Paid Project Opportunities
Once approved, graduates become eligible to begin accepting healthcare AI tasks and participating in paid remote AI projects available through the SCILabel platform.
Upon successful completion, graduates receive the SCI Healthcare AI Annotation Certificate with five-track endorsements, a verified SCILabel Tasker ID, and eligibility for onboarding into live remote healthcare AI projects through the SCILabel platform.
This program positions healthcare professionals at the forefront of one of the world’s fastest-growing industries — healthcare artificial intelligence — while creating flexible remote income opportunities compatible with ongoing clinical practice, research, or academic study.
Target audiences
- Clinical & Healthcare Professionals — Medical Doctors, Clinical Officers, Nurses, Midwives, Dentists, Pharmacists, Pharmacy Technicians, Physiotherapists, Occupational Therapists, Radiographers, Sonographers, Medical Imaging Specialists, Nutritionists & Dietitians
- Laboratory & Biomedical Professionals — Medical Laboratory Scientists, Biomedical Scientists, Biotechnologists, Biochemists, Molecular Biologists, Clinical Microbiologists, Histopathologists, Cytologists, Geneticists
- Public Health, Research & Health Informatics Professionals — Public Health Specialists, Epidemiologists, Clinical Researchers, Clinical Trials Coordinators, Health Informatics Specialists, Health Information Managers, Medical Coders, Biomedical Data Analysts
- Students & Academic Learners — Medical Students, Nursing Students, Pharmacy Students, Biomedical Science Students, Public Health Students, Biotechnology Students, Health Informatics Students, Healthcare and Life Sciences Students enrolled at certificate level or above
- Healthcare AI & Digital Health Professionals — Professionals interested in AI Model Evaluation & RLHF, Remote Healthcare AI Work Opportunities, Genomics & Precision Medicine AI, AI Ethics & Healthcare AI Governance, Digital Health & AI Career Transition Professionals
- Veterinary & One Health Professionals — Veterinary Doctors, Veterinary Laboratory Scientists, Veterinary Researchers, One Health Professionals
Features
- Comprehensive 5-Track Curriculum — Medical NLP & Clinical Text Annotation, Medical Imaging & Computer Vision, RLHF & Clinical AI Evaluation, Genomics & Biomedical Intelligence, AI Safety, Ethics & Regulatory Compliance
- Real-World Industry Training — Practical assignments mirror live healthcare AI annotation projects used by international SCILabel clients
- Live Portfolio Development — Learners contribute to internal SCILabel projects, building a verified professional portfolio during training
- Healthcare-Specific AI Training — Designed exclusively for healthcare and clinical professionals, not general-purpose annotators
- Flexible Online Learning — Fully online, self-paced structure, accessible globally and compatible with clinical/academic schedules
- Microsoft Teams & Canva LMS Integration — Enterprise learning system using Microsoft Teams for recording/evaluation and Canva LMS for learning delivery and assignments
- Practical Hands-On Assignments — 12 structured assignments covering NLP, medical imaging, RLHF evaluation, genomics, and AI safety/compliance
- SCILabel Platform Access — Includes annotation workspace access, demo projects, practice datasets, and real-world task simulation
- Industry-Relevant Annotation Tools — Exposure to Label Studio, CVAT, Labelbox, DICOM viewers, genomics tools, and clinical AI evaluation systems
- AI Safety & Regulatory Compliance Training — Covers HIPAA, GDPR, EU AI Act, FDA AI/ML guidance, WHO AI ethics, and healthcare data governance
- Career, Certification & Income Pathway — SCI certification with 5-track endorsement, SCILabel Tasker ID, international career preparation, and access to remote paid healthcare AI opportunities
Requirements
- Access to a Laptop/Desktop Computer — Windows, macOS, or Linux device required (mobile phones/tablets not recommended as primary devices)
- Reliable Internet Connection — Minimum 5 Mbps stable internet required for streaming lessons and uploading assignments
- Updated Web Browser — Google Chrome, Microsoft Edge, or Mozilla Firefox (latest version)
- Minimum Storage Capacity — At least 10 GB free storage space for datasets, lessons, and practical files
- Valid Email Access — Functional email address required for Canva LMS registration and official SCI communication
- Healthcare or Life Sciences Qualification — Certificate, diploma, or degree in a healthcare, biomedical, clinical, or life sciences field
- Student or Professional Eligibility — Current enrollment in a healthcare/life sciences program OR at least 1 year of relevant clinical/lab/research experience
- Core Digital & Language Skills — Basic computer literacy, email use, file management, and professional proficiency in written and spoken English
- Time Commitment — Ability to dedicate 1–2 hours daily, complete 60 lessons, submit 12 practicals, and attend final evaluation
- Professional & Ethical Conduct — Must sign SCI Code of Conduct, follow data confidentiality rules, use only authorized datasets, and comply with ethical AI annotation practices
Curriculum
- 6 Sections
- 109 Lessons
- 8 Weeks
Expand all sectionsCollapse all sections
- FOUNDATION PHASE (Lessons 1–15): Healthcare AI & Annotation FundamentalsAll learners complete this shared foundation before entering the specialist tracks. This phase builds a common language of AI principles, annotation fundamentals, the SCILabel platform, and the five-track programme structure — regardless of professional background.15
- 1.1Healthcare AI: The Global Opportunity & Your Role in It
- 1.2Machine Learning & Deep Learning for Clinical Professionals
- 1.3Healthcare Data Types: The Clinical Annotator’s Reference Atlas
- 1.4The Annotation Pipeline: From Raw Clinical Data to Trained AI Model
- 1.5Annotation Tools Masterclass: Platforms & Working Environments
- 1.6Annotation Quality, Inter-Rater Agreement & Professional Standards
- 1.7AI Ethics & Responsible Annotation Practice
- 1.8HIPAA, GDPR & Global Data Privacy for Annotators
- 1.9Clinical Terminology & Medical Abbreviations for Annotators
- 1.10Understanding EHR Systems & Clinical Workflows
- 1.11Quality Assurance in AI Annotation Projects
- 1.12Working as a Remote AI Annotator: Productivity & Professionalism
- 1.13Foundation Capstone: Integrating AI Knowledge & Platform Readiness
- 1.14Digital Workspace Setup, Canva LMS Navigation & Data Security
- 1.15SCILabel Platform Deep Dive & 5-Track Task Navigation
- TRACK 1(Lessons 16-40) : Medical Text Annotation & Natural Language ProcessingTrack 1 is compulsory for all learners and trains participants to annotate clinical text with the precision and clinical understanding that only a healthcare professional can provide. Learners annotate electronic health records, clinical notes, discharge summaries, adverse event records, and medical literature across 25 lessons, building the deepest possible NLP annotation skill set for global health-tech and pharma clients.25
- 2.1NLP in Healthcare: Applications, Architecture & Annotation Scope
- 2.2Clinical Text Types: Structure, Language & Annotation Context
- 2.3Medical Named Entity Recognition (NER) — Core Clinical Schema
- 2.4Medical NER — Advanced: Nested Entities, Multi-Word Spans & Rare Terms
- 2.5Clinical Relation Extraction & Temporal Annotation
- 2.6Assertion & Negation Classification in Clinical Text
- 2.7Adverse Event & Pharmacovigilance Text Annotation
- 2.8Clinical Text Classification & Document-Level Labeling
- 2.9PHI De-identification & Synthetic Clinical Data Generation
- 2.10Clinical Coding: ICD-10, SNOMED CT, LOINC & CPT Annotation
- 2.11EHR Data Annotation: HL7 FHIR, Structured Records & Biomedical Literature
- 2.12Prescription & Medication Record Annotation
- 2.13Clinical Trial & Research Protocol Text Annotation
- 2.14Radiology & Pathology Report Text Annotation
- 2.15Discharge Summary & Referral Letter Annotation
- 2.16Mental Health & Psychiatry Text Annotation
- 2.17Paediatric & Neonatal Clinical Text Annotation
- 2.18Surgical & Operative Note Annotation
- 2.19Social Determinants of Health (SDOH) Annotation
- 2.20Multilingual & Low-Resource Clinical Text Annotation
- 2.21NLP Annotation Quality Assurance & Inter-Annotator Agreement
- 2.22Building & Maintaining an NLP Annotation Style Guide
- 2.23Advanced NLP Tools: Active Learning & AI-Assisted Annotation
- 2.24Annotation Project Management for NLP Data Tasks
- 2.25Track 1 Capstone: NLP Portfolio Readiness & Client Demonstration
- TRACK 2(Lessons 16-40): Medical Image Labeling & Computer VisionTrack 2 is the clinical imaging core of the program and is available as an elective for learners with imaging, pathology, ophthalmology, or surgical backgrounds. Across 25 lessons, radiographers and imaging technologists lead annotation of radiology images, pathology slides, and ophthalmology scans using segmentation, bounding boxes, and diagnostic label assignment.20
- 3.1Medical Imaging Modalities: Clinical Overview for Annotators
- 3.2DICOM Format, Image Viewers & Clinical Annotation Environments
- 3.3Chest X-Ray Annotation: Normal Anatomy to Pathological Findings
- 3.4CT Scan Annotation: Slice Navigation, Windowing & Lesion Labeling
- 3.5MRI Annotation: Brain, Spine & Musculoskeletal
- 3.6Ultrasound & Echocardiography Image Annotation
- 3.7Digital Pathology & Whole Slide Image Annotation
- 3.8Ophthalmology Image Annotation: Retina, Anterior Segment & OCT
- 3.9Dermatology Image Annotation & Skin Lesion Classification
- 3.10Dental & Oral Radiology Annotation
- 3.11Surgical Video & Endoscopy Frame Annotation
- 3.12Advanced Segmentation Techniques for Medical Images
- 3.13Medical Image Annotation Quality Assurance
- 3.14Nuclear Medicine & PET/SPECT Annotation
- 3.15Paediatric Imaging Annotation Considerations
- 3.16Breast Imaging Annotation: Mammography & Breast MRI
- 3.17Cardiovascular Imaging Annotation
- 3.18Annotation Workflow Optimisation for High-Volume Image Projects
- 3.19AI-Assisted Image Annotation: Working with Pre-Annotations
- 3.20Track 2 Capstone: Multi-Modal Image Annotation Portfolio Readiness
- TRACK 3(Lessons 16-35): RLHF — Clinical AI Response EvaluationTrack 3 is the most commercially powerful elective track. Reinforcement Learning from Human Feedback (RLHF) is how the world’s largest AI companies continuously improve their large language models. Healthcare-domain RLHF evaluators are among the highest-value annotation professionals globally. Across 20 lessons, learners are trained to evaluate, rank, and provide corrective feedback on AI-generated clinical outputs.20
- 4.1RLHF Fundamentals: How Human Feedback Trains Clinical AI
- 4.2The Clinical AI Evaluation Framework: Accuracy, Safety & Helpfulness
- 4.3Evaluating AI Radiology & Pathology Report Outputs
- 4.4Evaluating AI Clinical Decision Support & Differential Diagnosis
- 4.5Evaluating AI Clinical Note Generation & Medical Summarisation
- 4.6Evaluating AI Drug Information, Prescribing & Pharmacovigilance Outputs
- 4.7Comparative Ranking & Preference Annotation for Clinical AI
- 4.8Red-Teaming Clinical AI: Flagging Harmful & Biased Outputs
- 4.9Prompt Engineering for Healthcare AI Evaluation Tasks
- 4.10Corrective Feedback Writing for AI Model Improvement
- 4.11RLHF Quality Assurance, Calibration & Expert Consistency
- 4.12Evaluating AI Mental Health & Psychiatry Outputs
- 4.13Evaluating AI Triage & Emergency Medicine Outputs
- 4.14Evaluating AI Responses for Chronic Disease Management
- 4.15Evaluating AI Nutrition, Lifestyle & Preventive Health Outputs
- 4.16Evaluating AI Outputs for Paediatric & Obstetric Care
- 4.17Building RLHF Evaluation Rubrics & Style Guides
- 4.18Clinical AI Chatbot Evaluation: Conversational Safety & Helpfulness
- 4.19High-Volume RLHF Annotation: Consistency & Workflow Management
- 4.20Track 3 Capstone: RLHF Portfolio Readiness & Enterprise AI Lab Demo
- TRACK 4:(Lessons 16-35): Genomics & Biomedical Data IntelligenceTrack 4 positions SCILabel at the frontier of precision medicine AI. Genomic and biomedical data annotation requires deep scientific knowledge that general-purpose annotators cannot provide. Across 20 lessons, this track is purpose-built for biotechnologists, biochemists, biomedical scientists, laboratory scientists, clinical researchers, and pharmacists.20
- 5.1Genomics & AI: Precision Medicine and the Annotation Opportunity52 Minutes
- 5.2Genomic Variant Annotation: SNPs, Indels, CNVs & Clinical Significance
- 5.3Gene Expression & Transcriptomics Data Annotation
- 5.4Protein Structure & Function Annotation for AI Drug Discovery
- 5.5Metabolomics, Proteomics & Biochemical Pathway Data Annotation
- 5.6Drug Safety & Pharmacovigilance Data Annotation
- 5.7Microbiome, Infectious Disease & One Health Data Annotation
- 5.8Biosignal & Wearable Health Data Annotation
- 5.9Clinical Trial Data Annotation & Biomedical Research Records
- 5.10Epigenomics & Chromatin Accessibility Data Annotation
- 5.11Single-Cell Sequencing Data Annotation
- 5.12Structural Variant & Copy Number Variation Annotation
- 5.13Biobank & Population Genomics Data Annotation
- 5.14Drug Discovery AI Pipeline Annotation
- 5.15Genomics Annotation Quality Assurance & Database Contribution
- 5.16Rare Disease & Orphan Drug Data Annotation
- 5.17Immunogenomics & HLA Typing Data Annotation
- 5.18Liquid Biopsy & ctDNA Data Annotation
- 5.19Bioinformatics Pipelines & Automated Annotation Review
- 5.20Track 4 Capstone: Genomics & Biomedical Annotation Portfolio Readiness
- TRACK 5:(Lessons 16-25):AI Safety, Ethics & Regulatory Compliance AnnotationTrack 5 addresses one of the fastest-growing global needs in AI: verifying that AI systems are safe, fair, unbiased, and compliant with regulatory requirements. Driven by the EU AI Act (2024), FDA AI/ML guidance, and WHO AI ethics frameworks, healthcare domain-expert AI safety evaluators are in urgent demand worldwide. Across 10 lessons, this track opens a new SCILabel project targeting regulators and AI governance bodies.9
- 6.1AI Safety in Healthcare: What Can Go Wrong & Why It Matters
- 6.2AI Bias in Healthcare: Detection, Classification & Annotation
- 6.3Healthcare Data Privacy: HIPAA, GDPR & and other Global Data Protection Acts
- 6.4The EU AI Act & FDA AI/ML Guidance for Healthcare Annotators
- 6.5WHO AI Ethics & Global Responsible AI Frameworks
- 6.6Clinical AI Risk Classification & Regulatory Labeling
- 6.7AI Safety Evaluation in Diagnostic AI Systems
- 6.8Fairness Auditing & Health Equity in AI Annotation
- 6.9Capstone Review: AI Safety Portfolio Readiness & Ethics Case Studies
Instructor
KSh5,500.00
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