About Course
The Healthcare AI Data Annotation & Evaluation Specialist Certification is a practical professional training programme designed for healthcare professionals who want to develop skills in healthcare data annotation, AI evaluation and quality assurance.
The programme introduces participants to the healthcare AI ecosystem and progressively develops practical skills in clinical text annotation, medical imaging, RLHF and AI response evaluation, biomedical and pharmaceutical data annotation, image segmentation, and annotation quality review.
Successful participants receive a certificate and may become eligible to access paid healthcare AI annotation and evaluation opportunities through SCILabel.
Learning Outcomes
By the end of the programme, participants should be able to:
- Explain the role of healthcare data annotation in developing AI systems.
- Apply annotation guidelines to clinical text, medical images and other healthcare datasets.
- Perform medical image annotation, classification and segmentation using appropriate tools and workflows.
- Evaluate AI-generated responses using criteria such as accuracy, relevance, safety and quality.
- Annotate biomedical, pharmaceutical, clinical research and related healthcare information.
- Provide clear and actionable corrective feedback for AI systems.
- Apply appropriate quality assurance and data verification practices.
- Recognise important privacy, safety, regulatory and ethical considerations when working with healthcare data.
- Work effectively within structured SCILabel annotation and evaluation workflows.
Course Content
Session 1— Foundations
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The global healthcare AI landscape — why clinical annotation is a growth profession
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What is data annotation, and why does it matter? An introduction for new taskers
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Why AI is important in healthcare: a brief survey for annotators
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How machine learning models learn: a non-technical guide for healthcare professionals
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GIGO — garbage in, garbage out: why annotation data quality determines model quality
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Healthcare data types: text, images, biosignals and structured records
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Clinical text types: SOAP notes, discharge summaries, referral letters and radiology reports — a format guide
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Named entity recognition (NER) in clinical text: the core schema (diseases, symptoms, medications, anatomy)
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Assertion and negation classification: identifying what is NOT present in clinical notes
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Advanced NER: nested entities, multi-word spans and rare terminology
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EHR systems explained: structure, workflow and where annotators fit in
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HIPAA in depth: the 18 Safe Harbor identifiers and what counts as PHI, with worked examples
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Practical Live Session 1
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Practical Live Session 2
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Practice Assessment 1